METHODS: Health policy experts from the public and private sectors were asked to participate in a survey to explore the current and future status of HTA implementation in Jordan. Semistructured interviews with senior policy makers supported by literature review were conducted to validate survey results and make recommendations for specific actions.

RESULTS: Survey and interview results indicated a need for increased HTA training, including both short courses and academic programs and gradually increasing public funding for technology assessment and appraisal. Multiple HTA bodies with central coordination can be the most feasible format of HTA institutionalization. The weight of cost-effectiveness criterion based on local data with published reports and explicit decision thresholds should be increased in policy decisions of pharmaceutical and nonpharmaceutical technologies.

OBJECTIVE: To assess the effects of pharmacist-led interventions within DMTAC on the outcomes of patients with type 2 diabetes mellitus in two distinct hospitals in Kedah, Malaysia.

METHODS: Patients with type 2 diabetes were randomly selected from the two hospitals included in this study. The study population was divided into two equal groups. The control group consisted of 200 patients receiving routine care from the hospitals. On the other hand, the intervention group included those patients with type 2 diabetes (200), who received separate counseling sessions from pharmacists in the DMTAC departments along with the usual treatment. The study lasted 1 year, during which both study groups participated in two distinct visits.

FINDINGS: A malaria survey spanning 7 years (2006 - 2012) was conducted in Selangor. A total of 1623 laboratory confirmed malaria cases were reported from Selangor's nine districts. While 72.6% of these cases (1178/1623) were attributed to imported malaria (cases originating from other countries), 25.5% (414/1623) were local cases and 1.9% (31/1623) were considered as relapse and unclassified cases combined. In this study, the most prevalent infection was P. vivax (1239 cases, prevalence 76.3%) followed by P. falciparum (211, 13.0%), P. knowlesi (75, 4.6%), P. malariae (71, 4.4%) and P. ovale (1, 0.06%). Mixed infections comprising of P. vivax and P. falciparum were confirmed (26, 1.6%). Entomological surveys targeting the residences of malaria patients' showed that the most commonly trapped Anopheles species was An. maculatus. No oocysts or sporozoites were found in the An. maculatus collected. Nevertheless, the possibility of An. maculatus being the malaria vector in the investigated locations was high due to its persistent occurrence in these areas.

OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.

RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.

OBJECTIVES: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the PFWZIC and PFDOSM methods.

METHODS: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the 'recipients list' and 'COVID-19 distribution criteria'. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM.

RESULTS: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values.