Background: Urban slums are characterised by unique challenging living conditions, which increase their inhabitants’ vulnerability to specific health conditions. The identification and prioritization of the key health issues occurring in these settings is essential for the development of programmes that aim to enhance the health of local slum communities effectively. As such, the present study sought to identify and prioritise the key health issues occurring in urban slums, with a focus on the perceptions of health professionals and community workers, in the rapidly growing city of Bangalore, India. Methods: The study followed a two-phased mixed methods design. During Phase I of the study, a total of 60 health conditions belonging to four major categories: - 1) non-communicable diseases; 2) infectious diseases; 3) maternal and women’s reproductive health; and 4) child health - were identified through a systematic literature review and semi-structured interviews conducted with health professionals and other relevant stakeholders with experience working with urban slum communities in Bangalore. In Phase II, the health issues were prioritised based on four criteria through a consensus workshop conducted in Bangalore. Results: The top health issues prioritized during the workshop were: diabetes and hypertension (non-communicable diseases category), dengue fever (infectious diseases category), malnutrition and anaemia (child health, and maternal and women’s reproductive health categories). Diarrhoea was also selected as a top priority in children. These health issues were in line with national and international reports that listed them as top causes of mortality and major contributors to the burden of diseases in India. Conclusions: The results of this study will be used to inform the development of technologies and the design of interventions to improve the health outcomes of local communities. Identification of priority health issues in the slums of other regions of India, and in other low and lower middle-income countries, is recommended.
BACKGROUND: Findings on the association between early high protein provision and mortality in ICU patients are inconsistent. The relation between early high protein provision and mortality in patients receiving CRRT remains unclear. The aim was to study the association between early high protein provision and hospital and ICU mortality and consistency in subgroups.METHODS: A retrospective cohort study was conducted in 2618 ICU patients with a feeding tube and mechanically ventilated ≥48 h (2003-2016). The association between early high protein provision (≥1.2 g/kg/day at day 4 vs. <1.2 g/kg/day) and hospital and ICU mortality was assessed for the total group, for patients receiving CRRT, and for non-septic and septic patients, by Cox proportional hazards analysis. Adjustments were made for APACHE II score, energy provision, BMI, and age.RESULTS: Mean protein provision at day 4 was 0.96 ± 0.48 g/kg/day. A significant association between early high protein provision and lower hospital mortality was found in the total group (HR 0.48, 95% CI 0.39-0.60, p = <0.001), CRRT-receiving patients (HR 0.62, 95% CI 0.39-0.99, p = 0.045) and non-septic patients (HR 0.56, 95% CI 0.44-0.71, p = <0.001). However, no association was found in septic patients (HR 0.71, 95% CI 0.39-1.29, p = 0.264). These associations were very similar for ICU mortality. In a sensitivity analysis for patients receiving a relative energy provision >50%, results remained robust in all groups except for patients receiving CRRT.CONCLUSIONS: Early high protein provision is associated with lower hospital and ICU mortality in ICU patients, including CRRT-receiving patients. There was no association for septic patients.
Analyzing historical decision-related data can help support actual operational decision-making processes. Decision mining can be employed for such analysis. This paper proposes the Decision Discovery Framework (DDF) designed to develop, adapt, or select a decision discovery algorithm by outlining specific guidelines for input data usage, classifier handling, and decision model representation. This framework incorporates the use of Decision Model and Notation (DMN) for enhanced comprehensibility and normalization to simplify decision tables. The framework’s efficacy was tested by adapting the C4.5 algorithm to the DM45 algorithm. The proposed adaptations include (1) the utilization of a decision log, (2) ensure an unpruned decision tree, (3) the generation DMN, and (4) normalize decision table. Future research can focus on supporting on practitioners in modeling decisions, ensuring their decision-making is compliant, and suggesting improvements to the modeled decisions. Another future research direction is to explore the ability to process unstructured data as input for the discovery of decisions.
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