METHODS: The purpose of this research was to identify the source of information, travel benefits and perceived risks related to movement of international patients and develop a conceptual model based on well-established theory. Thorough database search (Science Direct, utmj.org, nih.gov, nchu.edu.tw, palgrave-journals, medretreat, Biomedcentral) was performed to fulfill the objectives of the study.
RESULTS: International patients always concern about benefits and risks related to travel. These benefits and risks form images of destination in the minds of international patients. Different sources of information make international patients acquaint about the associated benefits and risks, which later leads to development of intention to visit. This conceptual paper helps in establishing model for decision-making process of international patients in developing visit intention.
CONCLUSION: Ample amount of literature is available detailing different factors involved in travel decision making of international patients; however literature explaining relationship between these factors is scarce.
Methods: A hybrid fuzzy multiple-criteria decision-making (FMCDM) process, consisting of the Analytic Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) method, is structured to aggregate the different criteria and rank different ELV alternatives in this complicated evaluation. In order to use the most profound knowledge and judgment of a professional expert team, this qualitative assessment highlights the importance of supportive information.
Results: The results obtained indicate that experts have considered the country-specific information as a reliable reference in their decisions. Among different key evaluation criteria in effluent standard setting, the highest experts' priority is "Environmental protection". For both the conventional and toxic pollutants, the influence of all other criteria namely "Economic feasibility", "Technology viability" and "Institutional capacity", as constraining criteria in developing countries, have not reduced the responsibility towards the environmental objectives. In ELVs ranking, experts have made their decisions with respect to the specific characteristics of each pollutant and the existing capacities and constraints of the country, without emphasizing on any specific reference.
Conclusions: This systematic and transparent approach has resulted in defensible country-specific ELVs for the Iron and Steel industry, which can be developed for other sectors. As the main conclusion, this paper demonstrates that FMCDM is a robust tool for this comprehensive assessment especially regarding the data availability limitations in developing countries.
METHODS: We will conduct a scoping review to identify and map evidence on how health equity is considered in economic evaluations of health interventions. We will search relevant electronic, gray literature and key journals. We developed a search strategy using text words and Medical Subject Headings terms related to health equity and economic evaluations of health interventions. Articles retrieved will be uploaded to reference manager software for screening and data extraction. Two reviewers will independently screen the articles based on their titles and abstracts for inclusion, and then will independently screen a full text to ascertain final inclusion. A simple numerical count will be used to quantify the data and a content analysis will be conducted to present the narrative; that is, a thematic summary of the data collected.
DISCUSSION: The results of this scoping review will provide a comprehensive overview of the current evidence on how health equity is considered in economic evaluations of health interventions and its research gaps. It will also provide key information to decision-makers and policy-makers to understand ways to include health equity into the prioritization of health interventions when aiming for a more equitable distribution of health resources.
SYSTEMATIC REVIEW REGISTRATION: This protocol was registered with Open Science Framework (OSF) Registry on August 14, 2019 (https://osf.io/9my2z/registrations).
METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks.
RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.