Hana Sulieman

Community pharmacy in the United Arab Emirates: Characteristics and workforce issues

Objectives  To determine the characteristics and workforce issues of community pharmacy practice in the United Arab Emirates (UAE).

Assessing patient satisfaction with community pharmacy in the UAE using a newly-validated tool

BACKGROUND Patient satisfaction has become an integral component of the quality of healthcare services. It has been used for the purpose of performance assessment, reimbursement, and quality management of health service delivery. It has been suggested that patient satisfaction could be a predictor of health-related behavior. OBJECTIVES To develop and validate a tool for use within the Arabic context to assess patient satisfaction. To assess patient satisfaction with current community pharmacy services in the UAE using the validated tool. METHODS A systematic process was used to develop an assessment tool that could be used within the Arabic context and establish its validity and reliability. Survey participants assessed their satisfaction with the services based on a 5-point Likert-type scale: Poor = 1, Fair = 2, Good = 3, Very good = 4, Excellent = 5. The anonymous questionnaire was distributed over a 5-month period to eligible participants in public places such as malls and shopping markets, in various emirates across the UAE. Those who were 21 years or older, taking at least one scheduled (regular) medication and having adequate Arabic or English language proficiency were included. RESULTS The instrument comprised four dimensions: Information, Relationship, Accessibility and Availability. Participants required more information about medications and self-management (Mean = 2.49 ± 1.19). Measures of competence, i.e., care, interest, time, confidence and trust, could also be improved (Mean = 3.05 ± 1.07). Accessibility scores measuring physical, geographical and financial items were lowest (Mean = 2.80 ± 1.33). Overall scores on availability of medications indicated relative satisfaction with this dimension (Mean = 3.51 ± 0.7). CONCLUSIONS This study is the first to use a patient satisfaction tool specifically developed for the Arabic context. Patient satisfaction scores in all dimensions were significantly lower than published data, suggesting patients have unmet expectations of community pharmacy services in the UAE. Stakeholders could utilize this information to help in the design and delivery of improved services that could lead to increased demand.

Community pharmacy services in the United Arab Emirates

Objectives  To identify the type and frequency of services provided through community pharmacies in the United Arab Emirates (UAE).

Intragroup Differences in Risk Factors for Breastfeeding Outcomes in a Multicultural Community

A sample of 221 women who delivered at Al Qassimi Hospital, Sharjah, United Arab Emirates, were included in this prospective study to identify breastfeeding patterns at day 1, 1 month, and 6 months postpartum. The exclusive breastfeeding rate was 76.5% on day 1, 48.4% at 1 month, and 13.3% at 6 months. At 6 months, 16.1% had stopped breastfeeding. Simple and multivariable binary logistic regression analyses were used to identify factors associated with better breastfeeding outcomes. Nationality significantly affected exclusive breastfeeding at day 1 and 1 month. Pethidine use was associated with lower levels of exclusive breastfeeding at 1 month. Education was the most significant determinant of breastfeeding behavior at 6 months. Effects of the interrelationships between factors were examined and shown to influence breastfeeding outcomes in different population subgroups. The findings of this study suggest that strategies to improve breastfeeding should focus on risk factors specific to the population subgroup.

Parametric sensitivity: A case study comparison

This article presents a comparative analysis of three derivative-based parametric sensitivity approaches in multi-response regression estimation: marginal sensitivity, profile-based approach developed by [Sulieman, H., McLellan, P.J., Bacon, D.W., 2004, A Profile-based approach to parametric sensitivity in multiresponse regression models, Computational Statistics & Data Analysis, 45, 721-740] and the commonly used approach of the Fourier Amplitude Sensitivity Test (FAST). We apply the classical formulation of FAST in which Fourier sine coefficients are utilized as sensitivity measures. Contrary to marginal sensitivity, profile-based and FAST approaches provide sensitivity measures that account for model nonlinearity and are pertinent to linear and nonlinear regression models. However, the primary difference between FAST and profile-based sensitivity is that traditional FAST fails to account for parameter dependencies in the model system while these dependencies are considered in the analysis procedure of profile-based sensitivity through the re-estimation of the remaining model parameters conditional on the values of the parameter of interest. An example is discussed to illustrate the comparisons by applying the three sensitivity methods to a model described by set of non-linear differential equations. Some computational aspects are also explored.

A profile-based approach to parametric sensitivity in multiresponse regression models

Abstract The profile-based sensitivity measure proposed by Sulieman et al. (Technometrics 43(4) (2001) 425) for single-response nonlinear models is extended to the case of multiresponse parameter estimation based on the Box–Draper estimation criterion. The profile-based sensitivity measure for the parameters in the multiresponse case is a vector-valued measure which simultaneously provides complete insight on the sensitivity behaviours of the M predicted responses in the model with respect to a parameter of interest. While providing first-order sensitivity information, the profile-based sensitivity coefficients, unlike the conventional sensitivity coefficients, also account for nonlinear parameter estimate co-dependencies derived from the Hessian of the determinant criterion, and are thus valid over broader ranges of parameter uncertainties. Two examples are discussed to illustrate applications of the extended measure.

Global derivative based sensitivity method for parameter estimation

Abstract In nonlinear parameter estimation local sensitivity assessment; conventionally measured by the first-order derivative of the predicted response with respect to a parameter of interest fails to provide a representative picture of the prediction sensitivity in the presence of significant parameter co-dependencies and/or nonlinearities. In this article we derive the profile-based sensitivity measure developed by Sulieman et al. (2001, 2004) [1,2] in the context of model re-parameterization. In particular, the so-called predicted response re-parameterization is shown to ultimately lead to the profile-based sensitivity coefficient defined by the total derivative of the model predicted response with respect to a parameter. Although inherently local, the profile-based measure is shown to handle simultaneous perturbations in parameter values while accounting for their co-dependencies. Thus the proposed measure possesses a central property of a global sensitivity measure and so it is considered hybrid local–global measure. The global Fourier amplitude sensitivity test (FAST) is added to the analysis and compared with both marginal and profile-based sensitivity methods. The Fourier sine amplitude is utilized here as a first-order sensitivity measure and shown to be directly linked to the local sensitivity coefficient averaged over all ranges of parameter uncertainties and so it is also considered hybrid local–global measure. The comparisons are explained by three compelling model cases with different degrees of parameter co-dependencies and nonlinearities.

The effects of repeated caesarean sections on maternal and fetal outcomes

Objectives : To determine (i) the effects of repeated caesarean sections on maternal and fetal outcomes (ii) whether these outcomes are affected by the timings of caesarean section (elective/emergency). Materials and Methods: This is a retrospective observational study conducted at Al Qassimi Hospital, Sharjah UAE from 1st Jan 2007 to 31st Dec 2008. 224 women who underwent caesarean section (CS) for two or more times were studied with respect to timing of current caesarean section, adhesions, condition of bladder and lower uterine segment, dehiscence of previous scar and any visceral injuries. Total blood loss and postoperative complications were also evaluated. Fetal parameters included gestational age at birth, APGAR scores and breathing difficulties if any. Results: Incidence of dense adhesions increased with increasing number of caesarean sections (22% for prev 2CS, 33% for prev 3 CS, 39% for prev 4 or more CS). Omental adhesions also followed similar pattern. The lower segment was thinned out in 38% of total patients. Scar dehiscence was seen in 50% of previous 4 caesarean section operated in emergency, in comparison to 4% and 6%% in previous 2 and 3 caesarean section. Other complications like bleeding, blood transfusion and postoperative complications were not statistically different in both the groups (elective and emergency). There was no case of caesarean hysterectomy and maternal death. The fetal outcome was similar in all the groups. Conclusions: No definitive upper limit of multiple repeat caesarean sections can be fixed for an individual woman based just on the number of previous Caesarean sections.

Sobol Sensitivity: A Strategy for Feature Selection

In this paper we propose a novel approach for feature selection in machine learning. The approach is based on the Sobol sensitivity analysis, a variance-based technique that determines the contribution of each feature and their interactions to the overall variance of the target variable. Similar to wrappers, Sobol sensitivity is a model-based approach that utilizes the trained model to evaluate feature importances. It uses the full feature set to train the model just as embedded methods do. Based on the trained model, it evaluates importance scores and, similar to filters, identifies the subset of important features with highest scores without retraining the model. The distinctive characteristic of the Sobol sensitivity approach is its computational efficiency compared to the existing feature selection algorithms. This is because importance scores for all individual features and subsets of features are calculated with the same trained model. We apply the proposed algorithm to a simulated data set and to four benchmark data sets used in machine learning literature. The results are compared to those obtained by two of the widely used feature selection algorithms and some computational aspects are also discussed.

Profile-based sensitivity in the design of experiments for parameter precision

D-optimal experimental designs for precise parameter estimation are designs which minimize the determinant of the variance-covariance matrix of the parameter estimates based on the conventional parametric sensitivity coefficients. These coefficients are local measures of sensitivity defined by the first-order derivative of system model function with respect to parameters of interest. For nonlinear models, linear sensitivity information fail to gouge the sensitivity behavior of the model and hence, the resulting determinant of variance-covariance matrix may not give a true indication of the volume of the joint inference region for system parameters. In this article, we employ the profile-based sensitivity coefficients developed by Sulieman et.al. (2001, 2004)in the D-optimal experimental designs. Profile-based sensitivity coefficients account for both model nonlinearity and parameter estimate correlations and are, therefore, expected to yield better precision of parameter estimates when used in the optimization of particular experimental design criteria. Some characteristics of the profile-based designs and related computational aspects are discussed. Application of the new designs to nonlinear model case is also presented.