Use of multicriteria decision analysis for assessing the benefit and risk of over-the-counter analgesics.

OBJECTIVES: To test the ability of a multicriteria decision analysis (MCDA) model to incorporate disparate data sources of varying quality along with clinical judgement in a benefit-risk assessment of six well-known pain-relief drugs. METHODS: Six over-the-counter (OTC) analgesics were evaluated against three favourable effects and eight unfavourable effects by seven experts who specialise in the relief of pain, two in a 2-day facilitated workshop whose input data and judgements were later peer-reviewed by five additional experts. KEY FINDINGS: Ibuprofen salts and solubilised emerged with the best benefit-risk profile, followed by naproxen, ibuprofen acid, diclofenac, paracetamol and aspirin. CONCLUSIONS: Multicriteria decision analysis enabled participants to evaluate the OTC analgesics against a range of favourable and unfavourable effects in a group setting that enabled all issues to be openly aired and debated. The model was easily communicated and understood by the peer reviewers, so the model should be comprehensible to physicians, pharmacists and other health professionals.

Environmental life cycle assessment of different domestic wastewater streams: policy effectiveness in a tropical urban environment.

To enhance local water security, the Singapore government promotes two water conservation policies: the use of eco-friendly toilets to reduce yellow water (YW) disposal and the installation of water efficient devices to minimize gray water (GW) discharge. The proposed water conservation policies have different impacts on the environmental performance of local wastewater management. The main purpose of this study is to examine and compare the impacts of different domestic wastewater streams and the effectiveness of two water conservation policies by means of life cycle assessment (LCA). LCA is used to compare three scenarios, including a baseline scenario (BL), YW-reduced scenario (YWR) and GW-reduced scenario (GWR). The BL is designed based on the current wastewater management system, whereas the latter two scenarios are constructed according to the two water conservation policies that are proposed by the Singapore government. The software SIMPARO 7.3 with local data and an eco-invent database is used to build up the model, and the functional unit is defined as the daily wastewater disposal of a Singapore resident. Due to local water supply characteristics, the system boundary is extended to include the sewage sludge management and tap water production processes. The characterization results indicate that the GWR has a significant impact reduction (22-25%) while the YWR has only a 2-4% impact reduction compared with the BL. The contribution analysis reveals that the GW dominates many impact categories except eutrophication potential. The tap water production is identified as the most influential process due to its high embodied energy demand in a local context. Life cycle costing analysis shows that both YWR and GWR are financially favorable. It is also revealed that the current water conservation policies could only achieve Singapore's short-term targets. Therefore, two additional strategies are recommended for achieving long-term goals. This study provides a comprehensive and reliable environmental profile of Singapore's wastewater management with the help of extended system boundary and local data. This work also fills the research gap of previous studies by identifying the contribution of different wastewater streams, which would serve as a good reference for source-separating sanitation system design.

Modeling brain activation in fMRI using group MRF.

Noise confounds present serious complications to functional magnetic resonance imaging (fMRI) analysis. The amount of discernible signals within a single dataset of a subject is often inadequate to obtain satisfactory intra-subject activation detection. To remedy this limitation, we propose a novel group Markov random field (GMRF) that extends each subject's neighborhood system to other subjects to enable information coalescing. A distinct advantage of GMRF over standard fMRI group analysis is that no stringent one-to-one voxel correspondence is required. Instead, intra- and inter-subject neighboring voxels are jointly regularized to encourage spatially proximal voxels to be assigned similar labels across subjects. Our proposed group-extended graph structure thus provides an effective means for handling inter-subject variability. Also, adopting a group-wise approach by integrating group information into intra-subject activation, as opposed to estimating a single average group map, permits inter-subject differences to be characterized and studied. GMRF can be elegantly implemented as a single MRF, thus enabling all subjects' activation maps to be simultaneously and collaboratively segmented with global optimality guaranteed in the case of binary labeling. We validate our technique on synthetic and real fMRI data and demonstrate GMRF's superior performance over standard fMRI analysis.