Psychometric Performance of HRQoL Measures: An Australian Paediatric Multi-Instrument Comparison Study Protocol (P-MIC)

Background: There is a lack of psychometric evidence about pediatric health-related quality of life (HRQoL) instruments. Evidence on cost effectiveness, involving the measurement of HRQoL, is used in many countries to make decisions about pharmaceuticals, technologies, and health services for children. Additionally, valid instruments are required to facilitate accurate outcome measurement and clinical decision making. A pediatric multi instrument comparison (P-MIC) study is planned to compare the psychometric performance and measurement characteristics of pediatric HRQoL instruments. Methods: The planned P-MIC study will collect data on approximately 6100 Australian children and adolescents aged 2–18 years via The Royal Children’s Hospital Melbourne and online survey panels. Participants will complete an initial survey, involving the concurrent collection of a range of pediatric HRQoL instruments, followed by a shorter survey 2–8 weeks later, involving the collection of a subset of instruments from the initial survey. Children aged ≥7 years will be asked to self-report HRQoL. Psychometric performance will be assessed at the instrument, domain, and item level. Conclusions: This paper describes the methodology of the planned P-MIC study, including benefits, limitations, and likely challenges. Evidence from this study will guide the choice of HRQoL measures used in clinical trials, economic evaluation, and other applications.

Combining EQ-5D-5L items into a level summary score: demonstrating feasibility using non-parametric item response theory using an international dataset

Background The EQ-5D-5L is a well-established health questionnaire that estimates health utilities by applying preference-based weights. Limited work has been done to examine alternative scoring approaches when utility weights are unavailable or inapplicable. We examined whether the Mokken scaling approach can elucidate 1) if the level summary score is appropriate for the EQ-5D-5L and 2) an interpretation of such a score. Methods The R package “mokken” was used to assess monotonicity (scaling coefficients H, automated item selection procedure) and manifest invariant item ordering (MIIO: paired item response functions [IRF], HT). We used a rich dataset (the Multiple Instrument Comparison, MIC) which includes EQ-5D-5L data from six Western countries. Results While all EQ-5D-5L items demonstrated monotonicity, the anxiety/depression (AD) item had weak scalability (Hi = 0.377). Without AD, scalability improved from Hs = 0.559 to Hs = 0.714. MIIO revealed that the 5 items can be ordered, and the ordering is moderately accurate in the MIC data (HT = 0.463). Excluding AD, HT improves to 0.743. Results were largely consistent across disease and country subgroups. Discussion The 5 items of the EQ-5D-5L form a moderate to strong Mokken scale, enabling persons to be ordered using the level summary score. Item ordering suggests that the lower range of the score represents mainly problems with pain and anxiety/depression, the mid-range indicates additional problems with mobility and usual activities, and middle to higher range of scores reveals additional limitations with self-care. Scalability and item ordering are even stronger when the anxiety/depression item is not included in the scale.

Electron spectroscopy with a diamond detector

An electronic grade single crystal chemical vapour deposition diamond was investigated as a prototype high temperature spectroscopic electron (β− particle) detector for future space science instruments. The diamond detector was coupled to a custom-built charge-sensitive preamplifier of low noise. A 63Ni radioisotope source (endpoint energy 66 keV) was used to provide a spectrum of β− particles incident on the detector. The operating temperature of the detector/preamplifier assembly was controlled to allow its performance to be investigated between + 100°C and − 20°C, in 20°C steps. Monte Carlo modelling was used to: a) calculate the β− particle spectrum incident on the detector; b) calculate the fraction of β− particle energy deposited into the detector; and c) predict the β− particle spectrum accumulated by the instrument. Comparison between the model and experimental data suggested that there was a 4.5 µm thick recombination region at the front of the detector. The spectrometer was demonstrated to be fully operable at temperatures, T, -20°C ≤ T ≤ 80°C; the results suggested that some form of polarisation phenomenon occurred in the detector at > 80°C. This article presents the first report of a calibrated low energy (⪅ 50 keV) spectroscopic β− particle diamond detector.

Correcting Biases in Historical Bathythermograph Data Using Artificial Neural Networks

Historical estimates of ocean heat content (OHC) are important for understanding the climate sensitivity of the Earth system and for tracking changes in Earth’s energy balance over time. Prior to 2004, these estimates rely primarily on temperature measurements from mechanical and expendable bathythermograph (BT) instruments that were deployed on large scales by naval vessels and ships of opportunity. These BT temperature measurements are subject to well-documented biases, but even the best calibration methods still exhibit residual biases when compared with high-quality temperature datasets. Here, we use a new approach to reduce biases in historical BT data after binning them to a regular grid such as would be used for estimating OHC. Our method consists of an ensemble of artificial neural networks that corrects biases with respect to depth, year, and water temperature in the top 10 m. A global correction and corrections optimized to specific BT probe types are presented for the top 1800 m. Our approach differs from most prior studies by accounting for multiple sources of error in a single correction instead of separating the bias into several independent components. These new global and probe-specific corrections perform on par with widely used calibration methods on a series of metrics that examine the residual temperature biases with respect to a high-quality reference dataset. However, distinct patterns emerge across these various calibration methods when they are extrapolated to BT data that are not included in our cross-instrument comparison, contributing to uncertainty that will ultimately impact estimates of OHC.

Interlaboratory comparison of heavy metal testing in animal diagnostic specimens and feed using inductively coupled plasma–mass spectrometry

We compared inductively coupled plasma–mass spectrometry (ICP-MS) test results for the analysis of heavy metals (As, Ba, Cd, Hg, Pb, and Se) in pet foods and routine veterinary diagnostic specimens using intralaboratory and interlaboratory comparisons. Four laboratories, 1 principal laboratory and 3 collaborating laboratories, conducted instrument comparison (limit of detection [LOD], limit of quantification [LOQ], and linear dynamic range [LDR] on 24 data sets), in-house method comparison (accuracy and precision on 120 data sets), and interlaboratory comparison (reproducibility on 528 data sets using Horwitz equation analysis). Matrices tested included 2 types of pet food jerky treats (chicken and sweet potato), bovine blood, and bovine liver and kidney. The instrument comparison study confirmed that ICP-MS provided the sensitivity necessary for the analysis of all heavy metals tested at concentrations below the level of concern for routine diagnostic testing. The “in-house” method comparison samples, spiked at low (0.04 µg/g), medium (0.4 µg/g), and high (8.0 µg/g; note: the high validation level spike for mercury was 2 µg/g) concentration levels, indicated that ICP-MS can meet U.S. FDA acceptance criteria for both accuracy (90–105% recovery) and precision (< 6% coefficient of variation). The interlaboratory comparison studies showed that ICP-MS is a reproducible method for the analysis of heavy metals (HorRat value of 0.5–2.0) except for mercury in one laboratory, which used a different sample preparation method (open block rather than microwave digestion). Overall, our study showed that ICP-MS is a reproducible method for the analysis of heavy metals in spite of minor differences in methodology.

Editorial for Special Issue “Remote Sensing of Precipitation”

This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor.