A Comparison of Items and Constructs of Standardized Health-Related Quality of Life and Mental Well-Being Measures.

OBJECTIVES: This study aimed to explore the internal constructs of the concepts being measured by EQ-5D-5L (a health-related quality of life measure that can produce preference-based utility values) and the 12-item General Health Questionnaire (GHQ-12, a mental well-being measure) and to understand to what extent the items of EQ-5D-5L and GHQ-12 associate with each other. METHODS: We used data from 12 701 respondents participating in a Belgian survey in 2022. Correlation coefficients between GHQ-12 and EQ-5D-5L were calculated at both the aggregate and item levels. Multidimensional scaling, exploratory factor analysis, and regression models were performed to investigate the underlying constructs that are associated with the items. RESULTS: Despite a moderate correlation (0.39) between the EQ-5D-5L and GHQ-12 total scores, only a trivial or weak correlation (<0.3) was observed between the first 4 EQ-5D-5L items and any GHQ-12 item. Multidimensional scaling and exploratory factor analysis showed the first 4 EQ-5D-5L dimensions were clustered together with EuroQol visual analog scale and positively phrased GHQ-12 items were close to each other, whereas EQ-anxiety/depression and negatively phrased GHQ-12 items were grouped with overall life satisfaction. In the regression models, not all GHQ-12 items had a significant coefficient to predict EQ-5D-5L responses. CONCLUSIONS: To the best of our knowledge, we present the first comparison of items and underlying constructs of GHQ-12 and EQ-5D-5L. The results showed that GHQ-12 can only partially predict the responses of EQ-5D-5L and the 2 instruments measure different constructs. Researchers should carefully consider conceptual legitimacy while applying the mapping technique and consider sensitivity analyses for the mapping estimates.

Psychological symptoms do not discriminate between reflux phenotypes along the organic-functional refractory GERD spectrum.

OBJECTIVE: Historically, psychological processes are associated with disorders at the functional end of the gastro-oesophageal reflux disease (GERD) spectrum. However, recent research suggests that psychological symptoms are relevant across the entire GERD spectrum. We aim to investigate whether psychological symptoms are associated with reflux phenotype (True GERD, Borderline GERD, reflux hypersensitivity, functional heartburn) along the GERD spectrum in a cohort of refractory reflux patients. DESIGN: Consecutive adult patients with refractory reflux symptoms underwent standard 24-hour pH-impedance monitoring and completed questionnaires assessing demographic, clinical and psychological information. Bayesian one-way analysis of variance assessed whether psychological variables differed across reflux phenotypes. Next, we applied multinomial and ordinal logistic regressions with clinical, demographic and psychological variables set as independent variables and reflux phenotype as the outcome variable. The complementary machine-learning approach entered all demographic, clinical and psychological variables into models, with reflux phenotype set nominally and ordinally. Cross-validated model performance was used to select the best model. RESULTS: 393 participants (mean (SD) age=48.5 (14.1); 60% female) were included. The Bayesian analyses found no difference in psychological variables across reflux phenotypes. Similarly, age, gender and proton pump inhibitor use were the only significant variables in the multinomial logistic regression and body mass index was significant in both regressions. Machine-learning analyses revealed poorly performing models with high misclassification rates (67-68%) in both models. CONCLUSION: Psychological symptoms do not differ between nor predict reflux phenotype membership in refractory reflux patients. Findings suggest that psychological symptoms are relevant across the spectrum of GERD, rather than specific to functional oesophageal disorders.

A linear mixed model to estimate COVID-19-induced excess mortality.

The Corona Virus Disease (COVID-19) pandemic has increased mortality in countries worldwide. To evaluate the impact of the pandemic on mortality, the use of excess mortality rather than reported COVID-19 deaths has been suggested. Excess mortality, however, requires estimation of mortality under nonpandemic conditions. Although many methods exist to forecast mortality, they are either complex to apply, require many sources of information, ignore serial correlation, and/or are influenced by historical excess mortality. We propose a linear mixed model that is easy to apply, requires only historical mortality data, allows for serial correlation, and down-weighs the influence of historical excess mortality. Appropriateness of the linear mixed model is evaluated with fit statistics and forecasting accuracy measures for Belgium and the Netherlands. Unlike the commonly used 5-year weekly average, the linear mixed model is forecasting the year-specific mortality, and as a result improves the estimation of excess mortality for Belgium and the Netherlands.

Pneumococcal vaccination coverage and adherence to recommended dosing schedules in adults: a repeated cross-sectional study of the INTEGO morbidity registry.

BACKGROUND: Since 2014, Belgium's Superior Health Council has recommended pneumococcal vaccination for adults aged 19-85 years at increased risk for pneumococcal diseases with a specific vaccine administration sequence and timing. Currently, Belgium has no publicly funded adult pneumococcal vaccination program. This study investigated the seasonal pneumococcal vaccination trends, evolution of vaccination coverage and adherence to the 2014 recommendations. METHODS: INTEGO is a general practice morbidity registry in Flanders (Belgium) that represents 102 general practice centres and comprised over 300.000 patients in 2021. A repeated cross-sectional study was performed for the period between 2017 and 2021. Using adjusted odds ratios computed via multiple logistic regression, the association between an individual's characteristics (gender, age, comorbidities, influenza vaccination status and socioeconomic status) and schedule-adherent pneumococcal vaccination status was assessed. RESULTS: Pneumococcal vaccination coincided with seasonal flu vaccination. The vaccination coverage in the population at risk decreased from 21% in 2017 to 18.2% in 2018 and then started to increase to 23.6% in 2021. Coverage in 2021 was highest for high-risk adults (33.8%) followed by 50- to 85-year-olds with comorbidities (25.5%) and healthy 65- to 85-year-olds (18.7%). In 2021, 56.3% of the high-risk adults, 74.6% of the 50+ with comorbidities persons, and 74% of the 65+ healthy persons had an adherent vaccination schedule. Persons with a lower socioeconomic status had an adjusted odds ratio of 0.92 (95% Confidence Interval (CI) 0.87-0.97) for primary vaccination, 0.67 (95% CI 0.60-0.75) for adherence to the recommended second vaccination if the 13-valent pneumococcal conjugate vaccine was administered first and 0.86 (95% CI 0.76-0.97) if the 23-valent pneumococcal polysaccharide vaccine was administered first. CONCLUSION: Pneumococcal vaccine coverage is slowly increasing in Flanders, displaying seasonal peaks in sync with influenza vaccination campaigns. However, with less than one-fourth of the target population vaccinated, less than 60% high-risk and approximately 74% of 50 + with comorbidities and 65+ healthy persons with an adherent schedule, there is still much room for improvement. Furthermore, adults with poor socioeconomic status had lower odds of primary vaccination and schedule adherence, demonstrating the need for a publicly funded program in Belgium to ensure equitable access.

A spatial model to jointly analyze self-reported survey data of COVID-19 symptoms and official COVID-19 incidence data.

This work presents a joint spatial modeling framework to improve estimation of the spatial distribution of the latent COVID-19 incidence in Belgium, based on test-confirmed COVID-19 cases and crowd-sourced symptoms data as reported in a large-scale online survey. Correction is envisioned for stochastic dependence between the survey's response rate and spatial COVID-19 incidence, commonly known as preferential sampling, but not found significant. Results show that an online survey can provide valuable auxiliary data to optimize spatial COVID-19 incidence estimation based on confirmed cases in situations with limited testing capacity. Furthermore, it is shown that an online survey on COVID-19 symptoms with a sufficiently large sample size per spatial entity is capable of pinpointing the same locations that appear as test-confirmed clusters, approximately 1 week earlier. We conclude that a large-scale online study provides an inexpensive and flexible method to collect timely information of an epidemic during its early phase, which can be used by policy makers in an early phase of an epidemic and in conjunction with other monitoring systems.

Disease mapping method comparing the spatial distribution of a disease with a control disease.

Small-area methods are being used in spatial epidemiology to understand the effect of location on health and detect areas where the risk of a disease is significantly elevated. Disease mapping models relate the observed number of cases to an expected number of cases per area. Expected numbers are often calculated by internal standardization, which requires both accurate population numbers and disease rates per gender and/or age group. However, confidentiality issues or the absence of high-quality information about the characteristics of a population-at-risk can hamper those calculations. Based on methods in point process analysis for situations without accurate population data, we propose the use of a case-control approach in the context of lattice data, in which an unrelated, spatially unstructured disease is used as a control disease. We correct for the uncertainty in the estimation of the expected values, which arises by using the control-disease's observed number of cases as a representation of a fraction of the total population. We apply our methods to a Belgian study of mesothelioma risk, where pancreatic cancer serves as the control disease. The analysis results are in close agreement with those coming from traditional disease mapping models based on internally standardized expected counts. The simulation study results confirm our findings for different spatial structures. We show that the proposed method can adequately address the problem of inaccurate or unavailable population data in disease mapping analysis.

On the impact of residential history in the spatial analysis of diseases with a long latency period: A study of mesothelioma in Belgium.

Mesothelioma is a rare cancer caused by exposure to asbestos. Belgium has a known long history of asbestos production, resulting in one of the highest mesothelioma mortality rates worldwide. While the production of asbestos has stopped completely, the long latency period of mesothelioma, which can fluctuate between 20 and 40 years after exposure, causes incidences still to be frequent. Mesothelioma's long incubation time affects our assessment of its geographical distribution as well. Since patients' residential locations are likely to change a number of times throughout their lives, the location where the patients develop the disease is often far from the location where they were exposed to asbestos. Using the residential history of patients, we propose the use of a convolution multiple membership model (MMM), which includes both a spatial conditional autoregressive and an unstructured random effect. Pancreatic cancer patients are used as a control population, reflecting the population at risk for mesothelioma. Results show the impact of the residential mobility on the geographical risk estimation, as well as the importance of acknowledging the latency period of a disease. A simulation study was conducted to investigate the properties of the convolution MMM. The robustness of the results for the convolution MMM is assessed via a sensitivity analysis.

The bivariate combined model for spatial data analysis.

To describe the spatial distribution of diseases, a number of methods have been proposed to model relative risks within areas. Most models use Bayesian hierarchical methods, in which one models both spatially structured and unstructured extra-Poisson variance present in the data. For modelling a single disease, the conditional autoregressive (CAR) convolution model has been very popular. More recently, a combined model was proposed that 'combines' ideas from the CAR convolution model and the well-known Poisson-gamma model. The combined model was shown to be a good alternative to the CAR convolution model when there was a large amount of uncorrelated extra-variance in the data. Less solutions exist for modelling two diseases simultaneously or modelling a disease in two sub-populations simultaneously. Furthermore, existing models are typically based on the CAR convolution model. In this paper, a bivariate version of the combined model is proposed in which the unstructured heterogeneity term is split up into terms that are shared and terms that are specific to the disease or subpopulation, while spatial dependency is introduced via a univariate or multivariate Markov random field. The proposed method is illustrated by analysis of disease data in Georgia (USA) and Limburg (Belgium) and in a simulation study. We conclude that the bivariate combined model constitutes an interesting model when two diseases are possibly correlated. As the choice of the preferred model differs between data sets, we suggest to use the new and existing modelling approaches together and to choose the best model via goodness-of-fit statistics. Copyright © 2016 John Wiley & Sons, Ltd.

Toxicity profiles and solvent-toxicant interference in the planarian Schmidtea mediterranea after dimethylsulfoxide (DMSO) exposure.

To investigate hydrophobic test compounds in toxicological studies, solvents like dimethylsulfoxide (DMSO) are inevitable. However, using these solvents, the interpretation of test compound-induced responses can be biased. DMSO concentration guidelines are available, but are mostly based on acute exposures involving one specific toxicity endpoint. Hence, to avoid solvent-toxicant interference, we use multiple chronic test endpoints for additional interpretation of DMSO concentrations and propose a statistical model to assess possible synergistic, antagonistic or additive effects of test compounds and their solvents. In this study, the effects of both short- (1 day) and long-term (2 weeks) exposures to low DMSO concentrations (up to 1000 µl l(-1) ) were studied in the planarian Schmidtea mediterranea. We measured different biological levels in both fully developed and developing animals. In a long-term exposure set-up, a concentration of 500 µl l(-1) DMSO interfered with processes on different biological levels, e.g. behaviour, stem cell proliferation and gene expression profiles. After short exposure times, 500 µl l(-1) DMSO only affected motility, whereas the most significant changes on different parameters were observed at a concentration of 1000 µl l(-1) DMSO. As small sensitivity differences exist between biological levels and developmental stages, we advise the use of this solvent in concentrations below 500 µl l(-1) in this organism. In the second part of our study, we propose a statistical approach to account for solvent-toxicant interactions and discuss full-scale solvent toxicity studies. In conclusion, we reassessed DMSO concentration limits for different experimental endpoints in the planarian S. mediterranea.

Marginalized multilevel hurdle and zero-inflated models for overdispersed and correlated count data with excess zeros.

Count data are collected repeatedly over time in many applications, such as biology, epidemiology, and public health. Such data are often characterized by the following three features. First, correlation due to the repeated measures is usually accounted for using subject-specific random effects, which are assumed to be normally distributed. Second, the sample variance may exceed the mean, and hence, the theoretical mean-variance relationship is violated, leading to overdispersion. This is usually allowed for based on a hierarchical approach, combining a Poisson model with gamma distributed random effects. Third, an excess of zeros beyond what standard count distributions can predict is often handled by either the hurdle or the zero-inflated model. A zero-inflated model assumes two processes as sources of zeros and combines a count distribution with a discrete point mass as a mixture, while the hurdle model separately handles zero observations and positive counts, where then a truncated-at-zero count distribution is used for the non-zero state. In practice, however, all these three features can appear simultaneously. Hence, a modeling framework that incorporates all three is necessary, and this presents challenges for the data analysis. Such models, when conditionally specified, will naturally have a subject-specific interpretation. However, adopting their purposefully modified marginalized versions leads to a direct marginal or population-averaged interpretation for parameter estimates of covariate effects, which is the primary interest in many applications. In this paper, we present a marginalized hurdle model and a marginalized zero-inflated model for correlated and overdispersed count data with excess zero observations and then illustrate these further with two case studies. The first dataset focuses on the Anopheles mosquito density around a hydroelectric dam, while adolescents' involvement in work, to earn money and support their families or themselves, is studied in the second example. Sub-models, which result from omitting zero-inflation and/or overdispersion features, are also considered for comparison's purpose. Analysis of the two datasets showed that accounting for the correlation, overdispersion, and excess zeros simultaneously resulted in a better fit to the data and, more importantly, that omission of any of them leads to incorrect marginal inference and erroneous conclusions about covariate effects.

Geospatial patterns of excess mortality in Belgium: Insights from the first year of the COVID-19 pandemic.

OBJECTIVES: Belgium experienced multiple COVID-19 waves that hit various groups in the population, which changed the mortality pattern compared to periods before the pandemic. In this study, we investigated the geographical excess mortality trend in Belgium during the first year of the COVID-19 pandemic. METHODS: We retrieved the number of deaths and population data in 2020 based on gender, age, and municipality of residence, and we made a comparison with the mortality data in 2017-2019 using a spatially discrete model. RESULTS: Excess mortality was significantly associated with age, gender, and COVID-19 incidence, with larger effects in the second half of 2020. Most municipalities had higher risks of mortality with a number of exceptions in the northeastern part of Belgium. Some discrepancies in excess mortality were observed between the north and south regions. CONCLUSIONS: This study offers useful insight into excess mortality and will aid local and regional authorities in monitoring mortality trends.

When and where? Day-night alterations in wild boar space use captured by a generalized additive mixed model.

Wild boar (Sus scrofa), an abundant species across Europe, is often subjected to management in agro-ecosystems in order to control population size, or to scare them away from agricultural fields to safeguard crop yields. Wild boar management can benefit from a better understanding on changes in its space use across the diel cycle (i.e., diel space use) in relation to variable hunting pressures or other factors. Here, we estimate wild boar diel space use in an agro-ecosystem in central Belgium during four consecutive "growing seasons" (i.e., April-September). To achieve this, we fit generalized additive mixed models (GAMMs) to camera trap data of wild boar aggregated over 1-h periods. Our results reveal that wild boar are predominantly nocturnal in all of the hunting management zones in Meerdaal, with activity peaks around sunrise and sunset. Hunting events in our study area tend to take place around sunrise and sunset, while non-lethal human activities occur during sunlight hours. Our GAMM reveals that wild boar use different areas throughout the diel cycle. During the day, wild boar utilized areas in the centre of the forest, possibly to avoid human activities during daytime. During the night, they foraged near (or in) agricultural fields. A post hoc comparison of space use maps of wild boar in Meerdaal revealed that their diurnal and nocturnal space use were uncorrelated. We did not find sufficient evidence to prove that wild boar spatiotemporally avoid hunters. Finally, our work reveals the potential of GAMMs to model variation in space across 24-h periods from camera trap data, an application that will be useful to address a range of ecological questions. However, to test the robustness of this approach we advise that it should be compared against telemetry-based methods to derive diel space use.