Enhancing rigour in the validation of patient reported outcome measures (PROMs): bridging linguistic and psychometric testing

Abstract Background A strong consensus exists for a systematic approach to linguistic validation of patient reported outcome measures (PROMs) and discrete methods for assessing their psychometric properties. Despite the need for robust evidence of the appropriateness of measures, transition from linguistic to psychometric validation is poorly documented or evidenced. This paper demonstrates the importance of linking linguistic and psychometric testing through a purposeful stage which bridges the gap between translation and large-scale validation. Findings Evidence is drawn from a study to develop a Welsh language version of the Beck Depression Inventory-II (BDI-II) and investigate its psychometric properties. The BDI-II was translated into Welsh then administered to Welsh-speaking university students (n = 115) and patients with depression (n = 37) concurrent with the English BDI-II, and alongside other established depression and quality of life measures. A Welsh version of the BDI-II was produced that, on administration, showed conceptual equivalence with the original measure; high internal consistency reliability (Cronbach’s alpha = 0.90; 0.96); item homogeneity; adequate correlation with the English BDI-II (r = 0.96; 0.94) and additional measures; and a two-factor structure with one overriding dimension. Nevertheless, in the student sample, the Welsh version showed a significantly lower overall mean than the English (p = 0.002); and significant differences in six mean item scores. This prompted a review and refinement of the translated measure. Conclusions Exploring potential sources of bias in translated measures represents a critical step in the translation-validation process, which until now has been largely underutilised. This paper offers important findings that inform advanced methods of cross-cultural validation of PROMs.</p

Enhancing rigour in the validation of patient reported outcome measures (PROMs): bridging linguistic and psychometric testing

BACKGROUND: A strong consensus exists for a systematic approach to linguistic validation of patient reported outcome measures (PROMs) and discrete methods for assessing their psychometric properties. Despite the need for robust evidence of the appropriateness of measures, transition from linguistic to psychometric validation is poorly documented or evidenced. This paper demonstrates the importance of linking linguistic and psychometric testing through a purposeful stage which bridges the gap between translation and large-scale validation. FINDINGS: Evidence is drawn from a study to develop a Welsh language version of the Beck Depression Inventory-II (BDI-II) and investigate its psychometric properties. The BDI-II was translated into Welsh then administered to Welsh-speaking university students (n = 115) and patients with depression (n = 37) concurrent with the English BDI-II, and alongside other established depression and quality of life measures. A Welsh version of the BDI-II was produced that, on administration, showed conceptual equivalence with the original measure; high internal consistency reliability (Cronbach’s alpha = 0.90; 0.96); item homogeneity; adequate correlation with the English BDI-II (r = 0.96; 0.94) and additional measures; and a two-factor structure with one overriding dimension. Nevertheless, in the student sample, the Welsh version showed a significantly lower overall mean than the English (p = 0.002); and significant differences in six mean item scores. This prompted a review and refinement of the translated measure. CONCLUSIONS: Exploring potential sources of bias in translated measures represents a critical step in the translation-validation process, which until now has been largely underutilised. This paper offers important findings that inform advanced methods of cross-cultural validation of PROMs

Complementary Geochemical, mineralogical and microbiological analyses of materials collected on the Greenland Ice Sheet

This dataset comprises of geochemical, mineralogical and microbiological analyses of material collected on the southwestern margin of the Greenland Ice Sheet in 2016 and 2017. Stream water, melted ice and snow samples were collected and analysed for carbon, nitrogen, phosphorus, cation and anion concentrations, pH, conductivity, total dissolved solids (TDS), mineral phase and class abundances and Rare Earth Elements (REE). Microbial community composition was also analysed. In addition, the results of a nutrient incubation experiment are also presented.The data were collected as part of a project investigating drivers of glacial ice algal growth on the Greenland Ice Sheet. We acknowledge funding from UK Natural Environment Research Council Consortium Grant, Black and Bloom (NE/M020770/1, NE/M021025/1 and NE/S001670/1). LGB and SL acknowledge funding from the German Helmholtz Recruiting Initiative (award number: I-044-16-01). LGB, AMA, and MT were also supported through an ERC Synergy Grant (ʻDeep Purpleʼ grant # 856416) from the European Research Council (ERC

Complementary Geochemical, mineralogical and microbiological analyses of materials collected on the Greenland Ice Sheet

This data publication is supplementary material to McCutcheon et al. (2021): "Melting of the Greenland Ice Sheet is a leading cause of land-ice mass loss and cryosphere-attributed sea level rise. Blooms of pigmented glacier ice algae lower ice albedo and accelerate surface melting in the ice sheet’s southwest sector. Although glacier ice algae cause up to 13% of the surface melting in this region, the controls on bloom development remain poorly understood. Here we show a direct link between mineral phosphorus in surface ice and glacier ice algae biomass through the quantification of solid and fluid phase phosphorus reservoirs in surface habitats across the southwest ablation zone of the ice sheet. We demonstrate that nutrients from mineral dust likely drive glacier ice algal growth, and thereby identify mineral dust as a secondary control on ice sheet melting." Tables included in this data publication: Supplementary Table 1. Locations, dates and sample types collected for particulate analyses. Sites 4a and 4b were the base camp locations for 2016 and 2017, respectively. Supplementary Table 2. Results of a Tukey HSD test with a 95% family-wise confidence interval for Fv/Fm measurements made at 24 h and 120 h in the nutrient addition experiment. Supplementary Table 3. Results of a Tukey HSD test with a 95% family-wise confidence interval for rETRmax measurements made at 24 h and 120 h in the nutrient addition experiment. Supplementary Table 4. Glacier algal cell concentrations (cells·mL-1) at the end of the 120 h nutrient incubation experiment. Glacier algae assemblage used for the incubations had an initial mean cell concentration of 8.0 ± 2.1 103 cells·mL-1. Supplementary Table 5. Carbon, nitrogen, and phosphorus content of solid LAPs collected from melted surface ice. TC: total carbon. TOC: total organic carbon, IC: inorganic carbon, Pexch: exchangeable/loosely bound phosphorus, Pmin: mineral phosphorus, Porg: organic phosphorus. Supplementary Table 6. Mineral phase abundances in 2016 and 2017 particulate samples as determined by Rietveld refinement with powder X-ray diffraction data. Abundances given as weight percent of total mineral dust (n=20). Supplementary Table 7. Mineral class abundances in high algal biomass (Hbio) ice sampled across the ablation zone in 2016. Values listed in weight percent of total mineral dust % (+/- standard error where applicable). Two-sided t-test comparing of mineral class abundances between site 3 and 4a. Supplementary Table 8. Major cation and anion concentrations in the fluid phase and pH, conductivity and total dissolved solids (TDS) of supraglacial stream water and melted ice and snow samples. LOD: level of detection, LOQ: level of quantification, ND: no data. Supplementary Table 9. Number of raw and processed sequences after each quality filtering step for 16S, ITS2 and 18S. Supplementary Table 10. Table shows the full bacterial community composition with the taxonomic assignments of each ASV on the lowest possible level. Values represent the relative abundances of the 16S ASVs in percentage of the total number of sequences and collapsed on the species level. Values are rounded to one decimal place, thus “ 0. Supplementary Table 11. Table shows the full eukaryotic community composition collapsed into higher eukaryotic taxonomic groups. Values represent the relative abundance of the 18S ASVs in percentage of the total number of sequences and collapsed on the species level. Values are rounded to one decimal place, thus “ 0. Supplementary Table 12. Table shows the fungal community composition with the taxonomic assignments of the ten most abundant ASV on the lowest possible level. The representative sequences were blasted against NCBI and the closest accession number with the respective similarity were recorded. If several hits shared the similarity one hit was chosen as an example (“e.g.”). Values represent the relative abundance of the ITS2 ASVs in percentage of the total number of sequences. Values are rounded to one decimal place, thus “ 0. Supplementary Table 13. Table shows the full algal community composition with the taxonomic assignments of each ASV on the lowest possible level. Values represent the relative abundance of the 18S ASVs in percentage of the total number of sequences. All ASVs were blasted against NCBI and the closest accession number with the respective similarity were recorded. If several hits shared the similarity one hit was chosen as an example (“e.g.”). Values are rounded to one decimal place, hence “ 0. *Based on light microscopic identifications in Lutz et al. (2018), this ASV likely represents Mesotaenium sp. (99.4% similarity with M. berggrenii var. alaskana) and not Ancylonema nordenskioeldii despite the slightly higher similarity (99.6%). Supplementary Table 14. Rare Earth Element (REE) analysis concentrations (µg·g-1) for the mineral dust in particulate samples.,Scanning electron microscopy data was collected by J. McCutcheon using a Hitatchi 8230 SEM at the Leeds Electron Microscopy and Spectroscopy Centre (LEMAS), University of Leeds, UK. X-ray diffraction was conducted by J. McCutcheon using a Bruker D8 Advance Eco X-ray diffractometer (Bruker, Billerica, USA) with a Cu source at the University of Leeds, UK. ICP-MS was conducted by S. Reid using a Thermo Fisher iCAPQc ICP-MS at the University of Leeds, UK. Phosphorus was measured either using segmented flow-injection analysis (AutoAnalyser3, Seal Analytical), or for samples containing lower concentrations of phosphorus by A. Stockdale, using a 100 cm WPI Liquid Waveguide Capillary Cell in conjunction with an Ocean Optics USB2000+ spectrophotometer. Both analyses were conducted at the University of Leeds, UK. Ion chromatography was conducted by A. Viet-Hillebrand at the German Research Centre for Geosciences, Potsdam, Germany using a conductivity detector on a Dionex ICS 3000 system, equipped with an AS 11 HC Dionex analytical column. Carbon and nitrogen analysis was conducted by B. Plessen and S. Pinkerneil at German Research Centre for Geosciences, Potsdam, Germany using an NC2500 Carlo Erba elemental analyzer. Amplicon libraries were sequenced on the Illumina MiSeq using paired 300-bp reads at the University of Bristol Genomics Facility, Bristol, UK. Rare Earth element concentrations were measured by A. Vanderstraeten using HR-ICP-MS (ThermoFisher Element 2) at the Vrije Universiteit Brussel, Belgium. Particle size distribution was measured by K. Jurkschat using a DC24000 CPS disc centrifuge at Oxford Materials Characterisation Services, Oxford, UK.