Modelling health state preference data using fixed and random effects models

The use of preference-based measures of health in the measurement of Health Related Quality of Life has become widely used in health economics. Hence, the development of preference-based measures of health has been a major concern for researchers throughout the world. This study aims to model health state preference data using a new preference-based measure of health (the SF- 6D) and to suggest alternative models for predicting health state utilities using fixed and random effects models. It also seeks to investigate the problems found in the SF-6D and to suggest eventual changes to it.

Assessing mackerel scad, Decapterus macarellus, in Cape Verde: using a Bayesian approach to biomass dynamic modelling in a data-limited situation

The fisheries for mackerel scad, Decapterus macarellus, are particularly important in Cape Verde, constituting almost 40% of total catches at the peak of the fishery in 1997 and 1998 ( 3700 tonnes). Catches have been stable at a much lower level of about 2 100 tonnes in recent years. Given the importance of mackerel scad in terms of catch weight and local food security, there is an urgent need for updated assessment. Stock assessment was carried out using a Bayesian approach to biomass dynamic modelling. In order to tackle the problem of a non-informative CPUE series, the intrinsic rate of increase, r, was estimated separately, and the ratio B-0/X, initial biomass relative to carrying capacity, was assumed based on available information. The results indicated that the current level of fishing is sustainable. The probability of collapse is low, particularly in the short-term, and it is likely that biomass may increase further above B-msy, indicating a healthy stock level. It would appear that it is relatively safe to increase catches even up to 4000 tonnes. However, the marginal posterior of r was almost identical to the prior, indicating that there is relatively low information content in CPUE. This was also the case in relation to B-0/X There have been substantial increases in fishing efficiency, which have not been adequately captured by the measure used for effort (days or trips), implying that the results may be overly optimistic and should be considered preliminary. (c) 2006 Elsevier B.V. All rights reserved.

Combining sampling, profiling, remote sensing and modelling to efficiently monitoring estuarine and coastal water quality

Monitoring of coastal and estuarine water quality has been traditionally performed by sampling with subsequent laboratory analysis. This has the disadvantages of low spatial and temporal resolution and high cost. In the last decades two alternative techniques have emerged to overcome this drawback: profiling and remote sensing. Profiling using multi-parameter sensors is now in a commercial stage. It can be used, tied to a boat, to obtain a quick “picture” of the system. The spatial resolution thus increases from single points to a line coincident with the boat track. The temporal resolution however remains unchanged since campaigns and resources involved are basically the same. The need for laboratory analysis was reduced but not eliminated because parameters like nutrients, microbiology or metals are still difficult to obtain with sensors and validation measurements are still needed. In the last years the improvement in satellite resolution has enabled its use for coastal and estuarine water monitoring. Although spatial coverage and resolution of satellite images in the present is already suitable to coastal and estuarine monitoring, temporal resolution is naturally limited to satellite passages and cloud cover. With this panorama the best approach to water monitoring is to integrate and combine data from all these sources. The natural tools to perform this integration are numerical models. Models benefit from the different sources of data to obtain a better calibration. After calibration they can be used to extend spatially and temporally the methods resolution. In Algarve (South of Portugal) a monitoring effort using this approach is being undertaken. The monitoring effort comprises five different locations including coastal waters, estuaries and coastal lagoons. The objective is to establish the base line situation to evaluate the impact of Waste Water Treatment Plants design and retrofitting. The field campaigns include monthly synoptic profiling, using an YSI 6600 multi-parameter system, laboratory analysis and fixed stations. The remote sensing uses ENVISAT\MERIS Level 2 Full Resolution data. This data is combined and used with the MOHID modelling system to obtain an integrate description of the systems. The results show the limitations of each method and the ability of the modelling system to integrate the results and to produce a comprehensive picture of the system.

Hydroinformatics based decision.making integrating modelling, sampling and profiling: the Ria Formosa case

A Waste Water monitoring program aiming to help decision making is presented. The program includes traditional and inboard sensor sampling, hydrodynamic and water quality modeling and a GIS based database to help the decision making of manager authorities. The focus is in the quality of waters receiving discharges from Waste Water Treatment Plants. Data was used to feed model simulations and produce hydrodynamic, effluent dispersion and ecological results. The system was then used to run different scenarios of discharge flow, concentration and location. The results enable to access the current water quality state of the lagoon and are being used as a decision making tool by the waste water managers in the evaluation phase of the treatment plant project to decide the location and the level of treatment of the discharge.