Hussam Alshraideh

Knowledge, attitudes, and beliefs about epilepsy and their predictors among university students in Jordan

The aim of this cross-sectional study was to assess the knowledge about epilepsy and the attitudes toward people with epilepsy (PWE) and their predictors among university students in Jordan. A self-administered questionnaire was distributed in three of the largest public universities in Jordan, and a total of 500 questionnaires were collected from each university. The number of students who reported that they had heard or read about epilepsy was 1165 (77.6%), and their data were analyzed. A significant proportion of students thought that epilepsy could be caused by the evil spirit (31.5%) and the evil eye (28.1%) or that it could be a punishment from God (25.9%). Epilepsys most commonly reported treatment methods were the Holy Quran (71.4%), medications (71.3%), and herbs (29.3%). The most common negative attitudes toward PWE were that the students would refuse to marry someone with epilepsy (50.5%) and that children with epilepsy must join schools for persons with disabilities (44.4%). Male students, students of humanities, and students with a low socioeconomic status tended to have more negative attitudes toward PWE. In conclusion, many students have misconceptions about the causes, treatment, and nature of epilepsy, and students have moderate negative attitudes toward PWE. Universities should have health promotion programs to increase awareness of their students about major public health problems such as epilepsy.

Bayesian Modeling and Optimization of Functional Responses Affected by Noise Factors

Experiments in systems where each run generates a curve, that is, where the response of interest is a set of observed values of a function, are common in engineering. In this paper, we present a Bayesian predictive modeling approach for functional response systems. The goal is to optimize the shape, or profile, of the functional response. A robust parameter design scenario is assumed where there are controllable factors and noise factors that vary randomly according to some distribution. The approach incorporates the uncertainty in the model parameters in the optimization phase, extending earlier approaches by J. Peterson (in the multivariate regression case) to the functional response case based on a hierarchical two-stage mixed-effects model. The method is illustrated with real examples taken from the literature and with simulated data, and practical aspects related to model building and diagnostics of the assumed mixed-effects model are discussed.

Knowledge, Barriers and Attitudes Towards Breast Cancer Mammography Screening in Jordan

BACKGROUND Breast cancer is the most common type of cancer in Jordan. Current efforts are focused on annual campaigns aimed at increasing awareness about breast cancer and encouraging women to conduct mammogram screening. In the absence of regular systematic screening for breast cancer in Jordan, there is a need to evaluate current mammography screening uptake and its predictors, assess womens knowledge and attitudes towards breast cancer and screening mammograms and to identify barriers to this preventive service. MATERIALS AND METHODS This cross-sectional study was conducted in six governorates in Jordan through face- to-face interviews on a random sample of women aged 40 to 69 years. RESULTS A total of 507 participants with mean age of 46.8±7.8 years were interviewed. There was low participation rate in early detection of breast cancer practices. Breast self-examination, doctor examination and periodic mammography screening were reported by 34.9%, 16.8% and 8.6% of study participants, respectively. Additionally 3.8% underwent breast cancer screening at least once but not periodically, while 87.6% had never undergone mammography screening. Reported reasons for conducting the screening were: perceived benefit (50%); family history of breast cancer (23.1%); perceived severity (21.2%); and advice from friend or family member (5.8%). City residents have shown higher probability of undergoing mammogram than those who live in towns or villages. Results revealed negative perceptions and limited knowledge of study participants on breast cancer and breast cancer screening. The most commonly reported barriers for women who never underwent screening were: fear of results (63.8%); no support from surrounding environment (59.7); cost of the test (53.4%); and religious belief, i.e. Qadaa Wa Qadar (51.1%). CONCLUSIONS In the absence of regular systematic screening for breast cancer in Jordan, the uptake of this preventive service is very low. It is essential for the country of Jordan to work on applying regular systematic mammography screening for breast cancer. Additionally, there is a need for improvement in the current health promotion programmes targeting breast cancer screening. Other areas that could be targeted in future initiatives in this field include access to screening in rural areas and removal of current barriers.

Process Monitoring Using Hidden Markov Models

Autocorrelated data arise in a variety of processes. To statistically monitor such processes, special statistical tools are needed to account for these correlations. The most common set of tools for such purpose is the autoregressive integrated moving average (ARIMA) models. Implementation of ARIMA models requires a fair amount of background understanding of how these models work, because the model selection step is essential. In this paper, we propose a new monitoring technique based on the use of hidden Markov models. The proposed monitoring method is powerful yet simple to use technique because it only requires a basic knowledge in statistics. Simulation results show that the proposed method performs similar to the ARIMA models in terms of average run length for detecting out of control processes and false alarms. Copyright

Quality of Life and Psychological Well-Being of Colorectal Cancer Survivors in Jordan

BACKGROUND Colorectal ranked first among cancers reported in males and ranked second amongst females in Jordan, accounting for 12.7% and 10.5% of cancers in males and females, respectively. Colorectal cancer patients can suffer several consequences after treatment that include pain and fatigue, constipation, stoma complications, sexual problems, appearance and body-image concerns as well as psychological dysfunction. There is no published quantitative data on the health-related quality of life and psychological wellbeing of Jordanian colorectal cancer survivors. METHOD This project was a cross-sectional study of colorectal cancer survivors diagnosed in 2009 and 2010. Assessment was performed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30), the colorectal cancer specific module (EORTC QLQ-CR 29) and the Hospital Anxiety and Depression Scale (HADS). Data on potential predictors of scores were also collected. RESULTS A total of 241 subjects completed the study with mean age of 56.7±13.6. Males represented 52.3% of study participants. A majority of participants reported good to high overall health; the mean Global health score was 79.74± 23.31 with only 6.64% of study participants scoring less than 33.3%. The striking result in this study was that none of the study participants participated in a psychosocial support group; only 4 of them (1.7%) were even offered such support. The mean scores for HADS, depression score, and anxiety score were 8.25±9, 4.35±4.9 and 3.9±4.6, respectively. However, 77.1% of study participants were within the normal category for the depression score and 81.7% were within this category for anxiety score; 5.4% of participants had severe anxiety and 5.4% of them had severe depression. DISCUSSION Patients with colorectal cancer in Jordan have a good quality of life and psychological wellbeing scores when compared with patients from western countries. None of the colorectal cancer patients managed at the Ministry of Health received any formal counselling, or participated in psychological or social support programmes. This highlights the urgent need for a psychosocial support programme, psychological screening and consultations for patients diagnosed with colorectal cancer at the Ministry of Health Hospitals.

Gaussian Process Modeling and Optimization of Profile Response Experiments

Experiments where the response of interest is a curve or ‘profile’ arise in a variety of applications in engineering practice. In a recent paper (Journal of Quality Technology, 44, 2, pp. 117–135, 2012), a mixed-effects Bayesian approach was proposed for the Bayesian optimization of profile response systems, where a particular shape of the profile response defines desired properties of the product or process. This paper proposes an alternative spatio-temporal Gaussian random function process model for such profile response systems, which is more flexible with respect to the types of desired profile shapes that can be modeled and allows us to model profile-to-profile correlation, if this exists. The method is illustrated with real examples taken from the literature, and practical aspects related to model building and diagnostics are discussed. Copyright

Real-Time Statistical Modeling of Blood Sugar

Diabetes is considered a chronic disease that incurs various types of cost to the world. One major challenge in the control of Diabetes is the real time determination of the proper insulin dose. In this paper, we develop a prototype for real time blood sugar control, integrated with the cloud. Our system controls blood sugar by observing the blood sugar level and accordingly determining the appropriate insulin dose based on patient’s historical data, all in real time and automatically. To determine the appropriate insulin dose, we propose two statistical models for modeling blood sugar profiles, namely ARIMA and Markov-based model. Our experiment used to evaluate the performance of the two models shows that the ARIMA model outperforms the Markov-based model in terms of prediction accuracy.

A Gaussian Process Control Chart for Monitoring Autocorrelated Process Data

Autocorrelated data arises in a variety of processes. To statistically monitor such processes, special tools are needed to account for these correlations. In this paper, we propose a new monitoring technique based on the use of Gaussian process models. The proposed monitoring method is a powerful yet simple to use technique because it only requires a basic knowledge of statistics and optimization. Simulation results show that the performance of the proposed method is similar to that of the individuals control chart applied to independent process data. The proposed method is illustrated with real process data taken from the literature.

The Impact of Applying Quality Management Practices on Patient Centeredness in Jordanian Public Hospitals: Results of Predictive Modeling:

Targeting the patient’s needs and preferences has become an important contributor for improving care delivery, enhancing patient satisfaction, and achieving better clinical outcomes. This study aimed to examine the impact of applying quality management practices on patient centeredness within the context of health care accreditation and to explore the differences in the views of various health care workers regarding the attributes affecting patient-centered care. Our study followed a cross-sectional survey design wherein 4 Jordanian public hospitals were investigated several months after accreditation was obtained. Total 829 clinical/nonclinical hospital staff members consented for study participation. This sample was divided into 3 main occupational categories to represent the administrators, nurses, as well as doctors and other health professionals. Using a structural equation modeling, our results indicated that the predictors of patient-centered care for both administrators and those providing clinical care were participation in the accreditation process, leadership commitment to quality improvement, and measurement of quality improvement outcomes. In particular, perceiving the importance of the hospital’s engagement in the accreditation process was shown to be relevant to the administrators (gamma = 0.96), nurses (gamma = 0.80), as well as to doctors and other health professionals (gamma = 0.71). However, the administrator staff (gamma = 0.31) was less likely to perceive the influence of measuring the quality improvement outcomes on the delivery of patient-centered care than nurses (gamma = 0.59) as well as doctors and other health care providers (gamma = 0.55). From the nurses’ perspectives only, patient centeredness was found to be driven by building an institutional framework that supports quality assurance in hospital settings (gamma = 0.36). In conclusion, accreditation is a leading factor for delivering patient-centered care and should be on a hospital’s agenda as a strategy for continuous quality improvement.

Using decision trees to explore the association between the length of stay and potentially avoidable readmissions: A retrospective cohort study

ABSTRACT Background: There is a growing concern that reduction in hospital length of stay (LOS) may raise the rate of hospital readmission. This study aims to identify the rate of avoidable 30-day readmission and find out the association between LOS and readmission. Methods: All consecutive patient admissions to the internal medicine services (n = 5,273) at King Abdullah University Hospital in Jordan between 1 December 2012 and 31 December 2013 were analyzed. To identify avoidable readmissions, a validated computerized algorithm called SQLape was used. The multinomial logistic regression was firstly employed. Then, detailed analysis was performed using the Decision Trees (DTs) model, one of the most widely used data mining algorithms in Clinical Decision Support Systems (CDSS). Results: The potentially avoidable 30-day readmission rate was 44%, and patients with longer LOS were more likely to be readmitted avoidably. However, LOS had a significant negative effect on unavoidable readmissions. Conclusions: The avoidable readmission rate is still highly unacceptable. Because LOS potentially increases the likelihood of avoidable readmission, it is still possible to achieve a shorter LOS without increasing the readmission rate. Moreover, the way the DT model classified patient subgroups of readmissions based on patient characteristics and LOS is applicable in real clinical decisions.