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The importance of including a mental health dimension in a multimorbidity indicator: an analysis of Belgian health survey data
No full textBackground
Multimorbidity is a rising public health concern. Indicators that address these complex health conditions are often exclusively devoted to physical diseases. Because of their high disease burden, mental health disorders ought to be considered as well. This paper aims to measure the added value of including a mental health dimension in a population-based multimorbidity indicator and identify which mental health measures are most appropriate.
Methods
Secondary analyses were conducted on data from the Belgian Health Interview Survey 2018. We compared the prevalence of different multimorbidity indicators (MIs) in relation to health impact measures, such as quality of life (EQ-5D score) and activity limitation (GALI). The MIs differed as to the health conditions involved: one was based on physical conditions only; the other three included mental health dimensions that were either self-reported or assessed by a scale (GAD-7, PHQ-9, and GHQ-12). We performed linear and logistic regressions to assess the association between the MIs and the health correlates and compared the goodness of fit of the different models.
Results
MI prevalence was higher when including a mental health dimension assessed with the GHQ-12 (42.0%) and with the GAD-7 or the PHQ-9 (39.4%) as compared to physical conditions only (35.0%). Associations between the MI and health correlates were consistently stronger if the MI included a mental health dimension. The regression models with MI including the GAD-7 and PHQ-9 showed the strongest association between MI and the health correlates and also had the best goodness-of-fit measures.
Conclusions
MIs that only take physical conditions into account underestimate their impact on individuals’ lives. Including mental ill-health in an MI is key to linking it to health correlates.</p
Bridging the Gap between Field Experiments and Machine Learning: The EC H2020 B-GOOD Project as a Case Study towards Automated Predictive Health Monitoring of Honey Bee Colonies.
No full textHoney bee colonies have great societal and economic importance. The main challenge that beekeepers face is keeping bee colonies healthy under ever-changing environmental conditions. In the past two decades, beekeepers that manage colonies of Western honey bees () have become increasingly concerned by the presence of parasites and pathogens affecting the bees, the reduction in pollen and nectar availability, and the colonies’ exposure to pesticides, among others. Hence, beekeepers need to know the health condition of their colonies and how to keep them alive and thriving, which creates a need for a new holistic data collection method to harmonize the flow of information from various sources that can be linked at the colony level for different health determinants, such as bee colony, environmental, socioeconomic, and genetic statuses. For this purpose, we have developed and implemented the B-GOOD (Giving Beekeeping Guidance by computational-assisted Decision Making) project as a case study to categorize the colony’s health condition and find a Health Status Index (HSI). Using a 3-tier setup guided by work plans and standardized protocols, we have collected data from inside the colonies (amount of brood, disease load, honey harvest, etc.) and from their environment (floral resource availability). Most of the project’s data was automatically collected by the BEEP Base Sensor System. This continuous stream of data served as the basis to determine and validate an algorithm to calculate the HSI using machine learning. In this article, we share our insights on this holistic methodology and also highlight the importance of using a standardized data language to increase the compatibility between different current and future studies. We argue that the combined management of big data will be an essential building block in the development of targeted guidance for beekeepers and for the future of sustainable beekeeping.</p
Quantitative risk assessments of skin sensitization for 26 allergens in different consumer products in the Saudi market
No full textSafety assessment of the substances 'wax, rice bran, oxidised' and 'wax, rice bran, oxidised, calcium salt' for use in food contact materials.
No full textThe EFSA Panel on Food Contact Materials (FCM) assessed the safety of the substances ‘wax, rice bran, oxidised’ and ‘wax, rice bran, oxidised, calcium salt’, used as additives up to 0.3% in polyethylene terephthalate (PET), polyamide (PA), thermoplastic polyurethane (TPU), polylactic acid (PLA) and poly(vinyl chloride) (PVC) in contact with all food types for long-term storage at room temperature and below, after hot-fill and/or heating. The substances consist of the chemical classes wax esters, carboxylic acids, alcohols and calcium salts of acids, along with an unidentified organic fraction up to ■■■■■ w/w. Migration into 10% ethanol and 4% acetic acid was below 0.012 mg/kg for each chemical class, and about 0.001 mg/kg for the unidentified fraction. In isooctane, migration was up to 0.297 mg/kg food for wax esters, below 0.01 mg/kg food for the other chemical classes and about 0.02 mg/kg food for the unidentified fraction. The contact with dry food and food simulated by 20% ethanol were considered covered by the migration tests with aqueous simulants. Based on genotoxicity assays and compositional analyses, the constituents of the chemical classes did not raise a concern for genotoxicity. The potential migration of individual constituents or groups of chemically-related compounds of the unidentified fraction would result in exposures below (for aqueous food) and above (for fatty food) the threshold of toxicological concern for genotoxic carcinogens. Therefore, the FCM Panel concluded that the substances are not of safety concern for the consumer, if used as additives up to 0.3% w/w in PET, PLA and rigid PVC materials and articles intended for contact with all food types except for fatty foods, for long-term storage at room temperature and below, including hot-fill and/or heating up to 100°C for up to 2 h.</p
