Multivariate analysis of regional-scale geochemical data for environmental monitoring

A compositional multivariate approach is used to analyse regional scale soil geochemical data obtained as part of the Tellus Project generated by the Geological Survey Northern Ireland (GSNI). The multi-element total concentration data presented comprise XRF analyses of 6862 rural soil samples collected at 20cm depths on a non-aligned grid at one site per 2 km2. Censored data were imputed using published detection limits. Using these imputed values for 46 elements (including LOI), each soil sample site was assigned to the regional geology map provided by GSNI initially using the dominant lithology for the map polygon. Northern Ireland includes a diversity of geology representing a stratigraphic record from the Mesoproterozoic, up to and including the Palaeogene. However, the advance of ice sheets and their meltwaters over the last 100,000 years has left at least 80% of the bedrock covered by superficial deposits, including glacial till and post-glacial alluvium and peat. The question is to what extent the soil geochemistry reflects the underlying geology or superficial deposits. To address this, the geochemical data were transformed using centered log ratios (clr) to observe the requirements of compositional data analysis and avoid closure issues. Following this, compositional multivariate techniques including compositional Principal Component Analysis (PCA) and minimum/maximum autocorrelation factor (MAF) analysis method were used to determine the influence of underlying geology on the soil geochemistry signature. PCA showed that 72% of the variation was determined by the first four principal components (PC’s) implying “significant” structure in the data. Analysis of variance showed that only 10 PC’s were necessary to classify the soil geochemical data. To consider an improvement over PCA that uses the spatial relationships of the data, a classification based on MAF analysis was undertaken using the first 6 dominant factors. Understanding the relationship between soil geochemistry and superficial deposits is important for environmental monitoring of fragile ecosystems such as peat. To explore whether peat cover could be predicted from the classification, the lithology designation was adapted to include the presence of peat, based on GSNI superficial deposit polygons and linear discriminant analysis (LDA) undertaken. Prediction accuracy for LDA classification improved from 60.98% based on PCA using 10 principal components to 64.73% using MAF based on the 6 most dominant factors. The misclassification of peat may reflect degradation of peat covered areas since the creation of superficial deposit classification. Further work will examine the influence of underlying lithologies on elemental concentrations in peat composition and the effect of this in classification analysis.

UK consensus project on quality in palliative day services: developing a quality indicator set using the RAND/UCLA appropriateness method

Introduction
Evaluating quality of palliative day services is essential for assessing care across diverse settings, and for monitoring quality improvement approaches.

Aim
To develop a set of quality indicators for assessment of all aspects (structure, process and outcome) of care in palliative day services.

Methods
Using a modified version of the RAND/UCLA appropriateness method (Fitch et al., 2001), a multidisciplinary panel of 16 experts independently completed a survey rating the appropriateness of 182 potential quality indicators previously identified during a systematic evidence review. Panel members then attended a one day, face-to-face meeting where indicators were discussed and subsequently re-rated. Panel members were also asked to rate the feasibility and necessity of measuring each indicator.

Results
71 indicators classified as inappropriate during the survey were removed based on median appropriateness ratings and level of agreement. Following the panel discussions, a further 60 were removed based on appropriateness and feasibility ratings, level of agreement and assessment of necessity. Themes identified during the panel discussion and findings of the evidence review were used to translate the remaining 51 indicators into a final set of 27.

Conclusion
The final indicator set included information on rationale and supporting evidence, methods of assessment, risk adjustment, and recommended performance levels. Further implementation work will test the suitability of this ‘toolkit’ for measurement and benchmarking. The final indicator set provides the basis for standardised assessment of quality across services, including care delivered in community and primary care settings.

Reference

• Fitch K, Bernstein SJ, Aguilar MD, et al. The RAND/UCLA Appropriateness Method User’s Manual. Santa Monica, CA: RAND Corporation; 2001. http://www.rand.org/pubs/monograph_reports/MR1269