Occupational burden of disease in the Netherlands

Ongunstige arbeidsomstandigheden veroorzaken 3,9% (onzekerheidsmarge 1,5%-7,2%) van de totale ziektelast in Nederland. De ziektelast is een maat om het verlies aan gezondheid uit te drukken. Het combineert vroegtijdige sterfte, de mate van vóórkomen van gezondheidsproblemen en de ernst van de gezondheidsproblemen. De ongunstige arbeidsomstandigheden die leiden tot de meeste ongezondheid zijn werkdruk, beeldschermwerk en blootstelling aan stoffen. Deze omstandigheden kunnen leiden tot burn-out, depressie, KANS (klachten van arm, nek en schouder), COPD (chronisch obstructieve longziekten) en longkanker. In het rapport is de positieve invloed van arbeid op de gezondheid niet meegenomen. Ook in 2020 veroorzaken burn-out, depressie en KANS veel ziektelast in de werkzame beroepsbevolking, bij ongewijzigde economische omstandigheden, een pensioengerechtigde leeftijd van 65 jaar en bij ongewijzigd (arbo)beleid. In 2007 heeft het RIVM voor het eerst laten zien welke arbeidsgerelateerde aandoeningen veel ziektelast in Nederland veroorzaken met gegevens uit 2003. Het huidige rapport biedt een hernieuwde versie met data uit 2007, evenals een toekomstverkenning en een verkenning van de ziektelast per sector. Deze schattingen geven beleidsmakers inzicht in de invloed van arbeidsrisico's op de gezondheid van werknemers. Deze benadering geeft ook aanknopingspunten voor maatregelen om de ziektelast door deze aandoeningen te verminderen.Occupational health risks cause 3.9% (uncertainty 1.5%-7.2%) of the total burden of disease in the Netherlands. The concept of burden of disease is a measure to express the loss of health. It combines the time lost due to premature mortality, prevalence and seriousness of the health problems. A high workload, working with a computer and exposure to harmful chemicals are the most unfavourable working conditions leading to health problems. They contribute most to the occupational burden of disease caused by: burn-out, depression, complaints of arm, neck and shoulder (CANS), chronic obstructive pulmonary disease and lung cancer. The health benefits of work were not included in this report. In 2020, burn-out, depression and CANS also cause a high burden of disease in the working population, considering unchanged economical conditions, a retirement age of 65 and unchanged health and safety policy. In 2007, the RIVM showed for the first time which occupational conditions contributed most to the burden of disease in 2003. The current report provides an update of the occupational burden of disease with data from 2007, as well as a forecast to 2020 and an exploration of the burden of disease per occupational sector. These estimates give policy makers insight in the influence of occupational risks on the health of employees. The data offer starting points for measures to reduce the burden of disease caused by these complaints.SZ

The effects of representational format on learning combinatorics from an interactive computer simulation

The current study investigated the effects of different external representational formats on learning combinatorics and probability theory in an inquiry based learning environment. Five conditions were compared in a pre-test post-test design: three conditions each using a single external representational format (Diagram, Arithmetic, or Text), and two conditions using multiple representations (Text + Arithmetic or Diagram + Arithmetic). The major finding of the study is that a format that combines text and arithmetics was most beneficial for learning, in particular with regard to procedural knowledge, that is the ability to execute action sequences to solve problems. Diagrams were found to negatively affect learning and to increase cognitive load. Combining diagrams with arithmetical representations reduced cognitive load, but did not improve learning outcomes

Prediction models for childhood asthma: a systematic review

Background The inability to objectively diagnose childhood asthma before age five often results in both under‐treatment and over‐treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school‐age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school‐age asthma. Methods Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school‐age children (6‐13 years). Validation studies were evaluated as a secondary objective. Results Twenty‐four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression‐based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression‐based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62‐0.83). Conclusion Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school‐age asthma predictio

Kan de 'asthma control questionnaire' (ACQ) gebruikt worden om astmapatiënten met en zonder goede symptoomcontrole van elkaar te onderscheiden? Resultaten van een pilotstudie.

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