Measuring Comprehensive, General Health Literacy in the General Adult Population:The Development and Validation of the HLS19-Q12 Instrument in Seventeen Countries

Background: For improving health literacy (HL) by national and international public health policy, measuring population HL by a comprehensive instrument is needed. A short instrument, the HLS(19)-Q12 based on the HLS-EU-Q47, was developed, translated, applied, and validated in 17 countries in the WHO European Region. Methods: For factorial validity/dimensionality, Cronbach alphas, confirmatory factor analysis (CFA), Rasch model (RM), and Partial Credit Model (PCM) were used. For discriminant validity, correlation analysis, and for concurrent predictive validity, linear regression analysis were carried out. Results: The Cronbach alpha coefficients are above 0.7. The fit indices for the single-factor CFAs indicate a good model fit. Some items show differential item functioning in certain country data sets. The regression analyses demonstrate an association of the HLS(19)-Q12 score with social determinants and selected consequences of HL. The HLS(19)-Q12 score correlates sufficiently highly (r ≥ 0.897) with the equivalent score for the HLS(19)-Q47 long form. Conclusions: The HLS(19)-Q12, based on a comprehensive understanding of HL, shows acceptable psychometric and validity characteristics for different languages, country contexts, and methods of data collection, and is suitable for measuring HL in general, national, adult populations. There are also indications for further improvement of the instrument

Measuring Comprehensive, General Health Literacy in the General Adult Population: The Development and Validation of the HLS19-Q12 Instrument in Seventeen Countries

Background: For improving health literacy (HL) by national and international public health policy, measuring population HL by a comprehensive instrument is needed. A short instrument, the HLS19-Q12 based on the HLS-EU-Q47, was developed, translated, applied, and validated in 17 countries in the WHO European Region. Methods: For factorial validity/dimensionality, Cronbach alphas, confirmatory factor analysis (CFA), Rasch model (RM), and Partial Credit Model (PCM) were used. For discriminant validity, correlation analysis, and for concurrent predictive validity, linear regression analysis were carried out. Results: The Cronbach alpha coefficients are above 0.7. The fit indices for the single-factor CFAs indicate a good model fit. Some items show differential item functioning in certain country data sets. The regression analyses demonstrate an association of the HLS19-Q12 score with social determinants and selected consequences of HL. The HLS19-Q12 score correlates sufficiently highly (r ≥ 0.897) with the equivalent score for the HLS19-Q47 long form. Conclusions: The HLS19-Q12, based on a comprehensive understanding of HL, shows acceptable psychometric and validity characteristics for different languages, country contexts, and methods of data collection, and is suitable for measuring HL in general, national, adult populations. There are also indications for further improvement of the instrument

Navigational health literacy

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Study design

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Advanced statistical methods for geochemical mineral exploration

The detection and identification of mineralization in geochemical exploration contains many tasks that are strongly linked to statistics. A geochemical exploration project starts with sampling planning in the area under investigation in terms of an optimal sampling design. There are of course also several other considerations that need to be taken into account, most importantly the overall costs for sampling, which limits the number of samples to be collected. Once the samples are available, they are analyzed in a laboratory resulting in “geochemical data”, which are challenging by their nature. Typically, they are compositional and thus multivariate, spatially dependent, they usually come with detection limit issues, and different kinds of uncertainties are inherent in these data. The last point is particularly addressed with statistical quality control procedures, and this provides the basis for selecting the data that are finally used for the subsequent statistical analyses. Besides the data quality considerations, data preprocessing is the following important step. Since values below the lower or above the upper detection limit could affect subsequent multivariate data analyses, it is important to first replace these values by appropriately estimated numbers. While methods accounting for the compositional nature of the geochemical data are available to estimate values below the lower detection limit, a novel method dealing with values exceeding the upper detection limit is proposed. Since this regression based procedure acts in a multivariate sense, it has advantages over existing strategies such as replacing right-censored values simply by a constant. The main statistical task in geochemical exploration is to locate of mineralized zones and to identify the underlying lithogeochemical source. While exploratory data analysis techniques may support this process, they are usually not accounting for the compositional nature of the data. Thus, an unsupervised methodology is introduced which accounts for the log-ratios of all element pairs. Due to the presence of data uncertainties, not the original observations are considered for the log-ratios, but values obtained from smooth fits, derived from Generalized Additive Models (GAMs). A measure incorporating the overall curvature of a log-ratio pair is introduced to rank the pairs, and to indicate pathfinder elements vectoring towards the mineralization. The procedure is developed for cases where samples located on linear transects, and also extended to cases where samples are taken on a plane. Real geochemical exploration data sets are used to demonstrate the performance of the methods.12

Quantifying the impact of risk factors at railway level crossings using accident prediction models: A cross-country study

Railway level crossings are critical elements in railway and road networks with accident occurrences resulting in"br" fatal and severe injuries. In addition to the human loss, level crossing accidents also negatively impact rail"br" transport reliability and transport speed. For safety management, specific risk factors should be identified and"br" their impact on overall safety quantified. To this end, multivariate regression equations, commonly known as"br" accident prediction models, have been used in the study. The paper describes the development of accident"br" prediction models in three Central European countries (Czech Republic, Hungary and Austria), using samples of"br" data on railway level crossings with flashing lights. The models were used to quantify the impact of several risk"br" factors. The cross-country study design enabled comparison of obtained experience and drawing conclusions for"br" further development of both road and railway network safety management

pXRF Measurements on Soil Samples for the Exploration of an Antimony Deposit: Example from the Vendean Antimony District (France)

International audienceMineral exploration is increasingly challenging in inhabited areas. To evaluate the potential of soil analysis by pXRF (portable X-ray fluorescence) as a low-footprint exploration technique, we revisited a historic Sb district in an agricultural area and performed shallow-soil sampling (Ah and B horizons) along profiles across known veins to capture the endogenic geochemical anomaly signals. Despite an expected bias between pXRF measurements and laboratory analyses, the former effectively located the Sb veins, especially when using their multi-element capabilities. Composition data processing (CoDa) and horizon-selective sampling significantly improved the method's efficiency. On-site measurements allow dynamic sampling and mapping, helping with faster, cost-effective sample selection for further laboratory investigations. Based on this case study, where similar geochemical patterns were obtained for both horizons, application of an on-site approach to a humic horizon can increase survey efficiency and decrease impacts