Background: Research in maternity care is often conducted in mixed low and high-risk or solely high-risk populations. This limits generalizability to the low-risk population of pregnant women receiving care from Dutch midwives. To address this limitation, 24 midwifery practices in the Netherlands bring together routinely collected data from medical records of pregnant women and their offspring in the VeCaS database. This database offers possibilities for research of physiological pregnancy and childbirth. This study explores if the pregnant women in VeCaS are a representative sample for the national population of women who receive primary midwife-led care in the Netherlands. Methods: In VeCaS we selected a low risk population in midwife-led care who gave birth in 2015. We compared population characteristics and birth outcomes in this study cohort with a similarly defined national cohort, using Chi Square and two side t-test statistics. Additionally, we describe some birth outcomes and lifestyle factors. Results: Midwifery practices contributing to VeCaS are spread over the Netherlands, although the western region is underrepresented. For population characteristics, the VeCaS cohort is similar to the national cohort in maternal age (mean 30.4 years) and parity (nulliparous women: 47.1% versus 45.9%). Less often, women in the VeCaS cohort have a non-Dutch background (15.7% vs 24.4%), a higher SES (9.9% vs 23.7%) and live in an urbanised surrounding (4.9% vs 24.8%). Birth outcomes were similar to the national cohort, most women gave birth at term (94.9% vs 94.5% between 37 + 0–41+ 6 weeks), started labour spontaneously (74.5% vs 75.5%) and had a spontaneous vaginal birth (77.4% vs 77.6%), 16.9% had a home birth. Furthermore, 61.1% had a normal pre-pregnancy BMI, and 81.0% did not smoke in pregnancy. Conclusions: The VeCaS database contains data of a population that is mostly comparable to the national population in primary midwife-led care in the Netherlands. Therefore, the VeCaS database is suitable for research in a healthy pregnant population and is valuable to improve knowledge of the physiological course of pregnancy and birth. Representativeness of maternal characteristics may be improved by including midwifery practices from the urbanised western region in the Netherlands.
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During the COVID-19 pandemic, the bidirectional relationship between policy and data reliability has been a challenge for researchers of the local municipal health services. Policy decisions on population specific test locations and selective registration of negative test results led to population differences in data quality. This hampered the calculation of reliable population specific infection rates needed to develop proper data driven public health policy. https://doi.org/10.1007/s12508-023-00377-y
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BackgroundA valuable opportunity for reducing the fall incidence in hospitals, is alerting nurses when a patient is about to fall. For such a fall prevention system, more knowledge is needed on what occurs right before a fall. This can be achieved with a stereo camera that automatically detects (and records) dangerous situations.MethodsInpatients with a high risk of falling are selected for inclusion. A fall-risk questionnaire is administered and falls are logged during their stay. A stereo-camera (3D BRAVO-EagleEye system) is mounted in the ceiling and monitors the bed with surroundings. A baseline recording is made to improve the algorithms behind the alert system. When a fall or dangerous situation is detected, monitoring data preceding the incident is stored. Data is analyzed to assess 1) the quality of the system and 2) the prevalence of dangerous situations. Interviews with senior nurses are included in the evaluation.ResultsData collection is ongoing (Currently n=18; falls=1), and currently consists of ±62 hour of baseline recordings and ±24 hour of event-based recordings. These recordings include false positives as well as actual high risk situations.ConclusionsDespite the initial enthusiasm of the participating departments, inclusion of participants is slow, and the number of falls lower than expected. Possible explanations for this have been discussed with the involved senior nurses. With the monitoring data we gained more insight into the occurrence of dangerous situations, but to be able to reliably predict falls, more data on actual fallsshould be recorded.
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Everyone has the right to participate in society to the best of their ability. This right also applies to people with a visual impairment, in combination with a severe or profound intellectual and possibly motor disability (VISPIMD). However, due to their limitations, for their participation these people are often highly dependent on those around them, such as family members andhealthcare professionals. They determine how people with VISPIMD participate and to what extent. To optimize this support, they must have a good understanding of what people with disabilities can still do with their remaining vision.It is currently difficult to gain insight into the visual abilities of people with disabilities, especially those with VISPIMD. As a professional said, "Everything we can think of or develop to assess the functional vision of this vulnerable group will help improve our understanding and thus our ability to support them. Now, we are more or less guessing about what they can see.Moreover, what little we know about their vision is hard to communicate to other professionals”. Therefore, there is a need for methods that can provide insight into the functional vision of people with VISPIMD, in order to predict their options in daily life situations. This is crucial knowledge to ensure that these people can participate in society to their fullest extent.What makes it so difficult to get this insight at the moment? Visual impairments can be caused by a range of eye or brain disorders and can manifest in various ways. While we understand fairly well how low vision affects a person's abilities on relatively simple visual tasks, it is much more difficult to predict this in more complex dynamic everyday situations such asfinding your way or moving around during daily activities. This is because, among other things, conventional ophthalmic tests provide little information about what people can do with their remaining vision in everyday life (i.e., their functional vision).An additional problem in assessing vision in people with intellectual disabilities is that many conventional tests are difficult to perform or are too fatiguing, resulting in either no or the wrong information. In addition to their visual impairment, there is also a very serious intellectual disability (possibly combined with a motor impairment), which makes it even more complex to assesstheir functional vision. Due to the interplay between their visual, intellectual, and motor disabilities, it is almost impossible to determine whether persons are unable to perform an activity because they do not see it, do not notice it, do not understand it, cannot communicate about it, or are not able to move their head towards the stimulus due to motor disabilities.Although an expert professional can make a reasonable estimate of the functional possibilities through long-term and careful observation, the time and correct measurement data are usually lacking to find out the required information. So far, it is insufficiently clear what people with VZEVMB provoke to see and what they see exactly.Our goal with this project is to improve the understanding of the visual capabilities of people with VISPIMD. This then makes it possible to also improve the support for participation of the target group. We want to achieve this goal by developing and, in pilot form, testing a new combination of measurement and analysis methods - primarily based on eye movement registration -to determine the functional vision of people with VISPIMD. Our goal is to systematically determine what someone is responding to (“what”), where it may be (“where”), and how much time that response will take (“when”). When developing methods, we take the possibilities and preferences of the person in question as a starting point in relation to the technological possibilities.Because existing technological methods were originally developed for a different purpose, this partly requires adaptation to the possibilities of the target group.The concrete end product of our pilot will be a manual with an overview of available technological methods (as well as the methods themselves) for assessing functional vision, linked to the specific characteristics of the target group in the cognitive, motor area: 'Given that a client has this (estimated) combination of limitations (cognitive, motor and attention, time in whichsomeone can concentrate), the order of assessments is as follows:' followed by a description of the methods. We will also report on our findings in a workshop for professionals, a Dutch-language article and at least two scientific articles. This project is executed in the line: “I am seen; with all my strengths and limitations”. During the project, we closely collaborate with relevant stakeholders, i.e. the professionals with specific expertise working with the target group, family members of the persons with VISPIMD, and persons experiencing a visual impairment (‘experience experts’).
Het Godivapp Applied in Pediatric Primary care (GoAPP) project ontwikkelt, onderzoekt en realiseert de implementatie van een e-health applicatie voor uitwisseling van videomateriaal in zelfstandige praktijken (MKB) in de eerstelijnsgezondheidszorg. Voor een goede analyse van bewegingsproblemen bij baby?s uit risicogroepen is het van belang de motorische ontwikkeling te meten en te volgen in de tijd. Kinderfysiotherapeuten gebruiken hiervoor een observatie-instrument, de Alberta Infant Motor Scale (AIMS). In 2014 en 2015 heeft de GODIVA-onderzoeksgroep (GrOss motor Development of Infants using home Video registration with the AIMS) van Hogeschool Utrecht een methode ontworpen, waarbij de ontwikkeling gevolgd kan worden aan de hand van video?s gemaakt door ouders. De methode wordt door professionals gezien als een aanvulling op bestaande methoden, die het monitoring van kinderen doelmatiger en transparanter maakt. De methode past uitstekend in de huidige e-health ontwikkeling en zelfmanagement/empowerment van ouders. Voor research met de videomethode is een prototype applicatie ontwikkeld waarmee op veilige wijze de filmbeelden verstuurd kunnen worden en opgeslagen. Het prototype is nog niet geschikt voor gebruik binnen de beroepspraktijk. Eerstelijns Kinderfysiotherapiepraktijken zouden graag de applicatie gebruiken. Zij verwachten dat het een waardevolle uitbreiding is van hun mogelijkheden en een kans om als praktijk te innoveren. Zij zien, als zelfstandige ondernemers, echter ook belemmeringen, zoals ICT-ondersteuning en een passende tarifering van een videoconsult. Voor deze kleine bedrijven spelen ook betaalbaarheid en gebruiksgemak een essentiële rol. Binnen GoAPP zijn vijf perspectieven voor innovatie en implementatie van e-health bij elkaar gebracht: eindgebruikers, zorginhoudelijk, harde technologie, zachte technologie en bedrijfskundig perspectief. Georganiseerd rondom drie werkpakketten wordt interdisciplinair onderzoek gedaan naar (1) optimalisatie van het videoportal, (2) implementatie, en (3) bedrijfskundige haalbaarheid, via ontwerpgericht onderzoek, literatuuronderzoek, implementatieanalyse en business-case onderzoek. Een vierde werkpakket richt zich op doorgroei van het netwerk kinderfysiotherapeuten naar een Community of Practice. Doel: Een innovatieve videomethode voor het observeren van de motoriek van zuigelingen, geschikt voor eerstelijnspraktijken kinderfysiotherapie, met een passend implementatieplan en business modelling.
About half of the e-waste generated in The Netherlands is properly documented and collected (184kT in 2018). The amount of PCBs in this waste is projected to be about 7kT in 2018 with a growth rate of 3-4%. Studies indicate that a third of the weight of a PCB is made or recoverable and critical metals which we need as resources for the various societal challenges facing us in the future. Recycling a waste PCB today means first shredding it and then processing it for material recovery mostly via non-selective pyrometallurgical methods. Sorting the PCBs in quality grades (wastebins) before shredding would however lead to more flexibility in selecting when and which recovery metallurgy is to be used. The yield and diversity of the recovered metals increases as a result, especially when high-grade recycling techniques are used. Unfortunately, the sorting of waste PCBs is not easily automated as an experienced operator eye is needed to classify the very inhomogeneous waste-PCB stream in wastebins. In this project, a knowledge institution partners with an e-waste processor, a high-grade recycling technology startup and a developer of waste sorting systems to investigate the efficiency of methods for sensory sorting of waste PCBs. The knowledge gained in this project will lead towards a waste PCB sorting demonstrator as a follow-up project.
