Early termination in interdisciplinary pain rehabilitation:numbers, timing, and reasons. A mixed method study

PURPOSE: To analyse the number of, timing of, and reasons for early termination of interdisciplinary pain rehabilitation (IPR). METHODS: A multicentre study in two Dutch rehabilitation centres with a mixed method design. Quantitative part: retrospective patient file review of all IPR patients. Qualitative part: 20 semi-structured patient interviews with early IPR terminators. RESULTS: One hundred and thirty-seven of 428 participants (31.3%) had terminated IPR early, of which almost 30% had a positive reason. Of a planned treatment duration of 12 weeks, the median (interquartile range (IQR)) reduction was 5.3 week (3.0; 8.0). Over 80% of the early terminators with negative reasons stopped in the first half of IPR, whereas approximately 55% of the early terminators with positive reasons stopped in the final quarter of IPR. A discrepancy between patient expectations of the aim and content and the actual IPR was mentioned as a negative reason for early termination. Many of the positive early terminators were able to self-manage. CONCLUSIONS: Previously reported figures on early termination were confirmed. Early termination of IPR should not be considered negative per se, because a substantial proportion of early terminations have a positive reason. Negative early terminators tend to stop earlier during IPR, compared to positive terminators. Implications for rehabilitation Substantial rates of patients (31%) terminate interdisciplinary pain rehabilitation (IPR) earlier than planned. Early IPR termination should not be considered negative per se, because a substantial proportion of early terminations have a positive reason (i.e. goals achieved early). Although patients receive extensive personalised information about aim and content of IPR before starting, early terminators with a negative reason often have different expectations about the aim and content of treatment. Clinicians and researchers should be focused on how to explain IPR to the patient and check whether the patient has understood it well and is convinced of its credibility

The Practice of Letter Writing: Skills, Models, and Early Modern Dutch Manuals

Article / Letter to editorLeiden Univ Centre for LinguisticsLeiden Univ Centre for Linguistic

Riverbank macrolitter in the Dutch Rhine-Meuse delta

Anthropogenic litter in aquatic ecosystems negatively impacts ecosystems, species and economic activities. Rivers play a key role in transporting land-based waste towards the ocean. A large portion however is retained within river basins, for example in the estuary, in sediments and on the riverbanks. To effectively identify litter sources, sinks and transport mechanisms, reliable data are crucial. Furthermore, such data can support optimizing litter prevention mitigation and clean-up efforts. This paper presents the results of a 2-year monitoring campaign focused on riverbank macrolitter (>0.5 cm) in the Dutch Rhine-Meuse delta. Between 2017 and 2019, volunteers sampled 152 415 litter items at 212 unique locations. All items were categorized based on the River-OSPAR method (based on the OSPAR beach litter guidelines), which includes 110 specific item categories across ten parent categories. The median litter density was 2060 items/km, and the most observed items were foam, hard, and soft plastic fragments (55.8%). Plastic bottles, food wrappings and packaging, caps, lids and cotton swabs were the most abundant specific items. The litter density and most abundant items vary considerably between rivers, along the river, and over time. For both rivers however, the highest litter density values were found at the Belgian (Meuse) and German (Rhine) borders, and at the Biesbosch National Park, the most downstream location. With this paper, we aim to provide a first scientific overview of the abundance, top item categories, and spatiotemporal variation of anthropogenic litter on riverbanks in the Dutch Rhine-Meuse delta. In addition, we evaluate the used River-OSPAR method and provide suggestions for future implementation in (inter)national long-term monitoring strategies. The results can be used by scientists and policy-makers for future litter monitoring, prevention and clean-up strategies

Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

Coastlines potentially harbor a large part of litter entering the oceans, such as plastic waste. The relative importance of the physical processes that influence the beaching of litter is still relatively unknown. Here, we investigate the beaching of litter by analyzing a data set of litter gathered along the Dutch North Sea coast during extensive beach cleanup efforts between the years 2014 and 2019. This data set is unique in the sense that data are gathered consistently over various years by many volunteers (a total of 14 000) on beaches that are quite similar in substrate (sandy). This makes the data set valuable to identify which environmental variables play an important role in the beaching process and to explore the variability of beach litter concentrations. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that tides play an especially important role, where an increasing tidal variability and tidal height leads to less litter found on beaches. Relatively straight and exposed coastlines appear to accumulate more litter. The regression model indicates that transport of litter through the marine environment is also important in explaining beach litter variability. By understanding which processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment, such as organizing beach cleanups during low tides at exposed coastlines. We estimate that 16 500–31 200 kg (95 % confidence interval) of litter is located along the 365 km of Dutch North Sea coastline

Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

Coastlines potentially harbor a large part of litter entering the oceans, such as plastic waste. The relative importance of the physical processes that influence the beaching of litter is still relatively unknown. Here, we investigate the beaching of litter by analyzing a data set of litter gathered along the Dutch North Sea coast during extensive beach cleanup efforts between the years 2014 and 2019. This data set is unique in the sense that data are gathered consistently over various years by many volunteers (a total of 14 000) on beaches that are quite similar in substrate (sandy). This makes the data set valuable to identify which environmental variables play an important role in the beaching process and to explore the variability of beach litter concentrations. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that tides play an especially important role, where an increasing tidal variability and tidal height leads to less litter found on beaches. Relatively straight and exposed coastlines appear to accumulate more litter. The regression model indicates that transport of litter through the marine environment is also important in explaining beach litter variability. By understanding which processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment, such as organizing beach cleanups during low tides at exposed coastlines. We estimate that 16 500–31 200 kg (95 % confidence interval) of litter is located along the 365 km of Dutch North Sea coastline

The potential contribution of house crickets to the dietary zinc content and nutrient adequacy in young Kenyan children : A linear programming analysis using Optifood

Zinc deficiency arising from inadequate dietary intake of bioavailable zinc is common in children in developing countries. Because house crickets are a rich source of zinc, their consumption could be an effective public health measure to combat zinc deficiency. This study used Optifood, a tool based on linear programming analysis, to develop food-based dietary recommendations (FBR) and predict whether dietary house crickets can improve both zinc and overall nutrient adequacy of children's diets. Two quantitative, multi-pass 24-hour recalls from 47 children aged two and three years residing in rural Kenya were collected and used to derive model parameters, including a list of commonly consumed foods, median serving sizes, and frequency of consumption. Two scenarios were modelled: (i) FBR based on local available foods and (ii) FBR based on local available foods with house crickets. Results revealed that zinc would cease to be a problem nutrient when including house crickets to children's diets (PRI coverage for zinc increased from 89% to 121% in the best-case scenario). FBR based on both scenarios could ensure nutrient adequacy for all nutrients except for fat, but energy percentage (E%) for fat was higher when house crickets were included in the diet (23 E% versus 19 E%). This manoeuvre, combined with realistic changes in dietary practices, could therefore improve dietary zinc content and ensure adequacy for twelve nutrients for Kenyan children. Further research is needed to render these theoretical recommendations, practical

Disentangling Variability in Riverbank Macrolitter Observations

Anthropogenic macrolitter (>0.5 cm) in rivers is of increasing concern. It has been found to have an adverse effect on riverine ecosystem health, and the livelihoods of the communities depending on and living next to these ecosystems. Yet, little is known on how macrolitter reaches and propagates through these ecosystems. A better understanding of macrolitter transport dynamics is key in developing effective reduction, preventive, and cleanup measures. In this study, we analyzed a novel dataset of citizen science riverbank macrolitter observations in the Dutch Rhine-Meuse delta, spanning two years of observations on over 200 unique locations, with the litter categorized into 111 item categories according to the river-OSPAR protocol. With the use of regression models, we analyzed how much of the variation in the observations can be explained by hydrometeorology, observer bias, and location, and how much can instead be explained by temporal trends and seasonality. The results show that observation bias is very low, with only a few exceptions, in contrast with the total variance in the observations. Additionally, the models show that precipitation, wind speed, and river flow are all important explanatory variables in litter abundance variability. However, the total number of items that can significantly be explained by the regression models is 19% and only six item categories display an R2 above 0.4. This suggests that a very substantial part of the variability in macrolitter abundance is a product of chance, caused by unaccounted (and often fundamentally unknowable) stochastic processes, rather than being driven by the deterministic processes studied in our analyses. The implications of these findings are that for modeling macrolitter movement through rivers effectively, a probabilistic approach and a strong uncertainty analysis are fundamental. In turn, point observations of macrolitter need to be planned to capture short-term variability. Water Resource

Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

Coastlines potentially harbor a large part of litter entering the oceans, such as plastic waste. The relative importance of the physical processes that influence the beaching of litter is still relatively unknown. Here, we investigate the beaching of litter by analyzing a data set of litter gathered along the Dutch North Sea coast during extensive beach cleanup efforts between the years 2014 and 2019. This data set is unique in the sense that data are gathered consistently over various years by many volunteers (a total of 14 000) on beaches that are quite similar in substrate (sandy). This makes the data set valuable to identify which environmental variables play an important role in the beaching process and to explore the variability of beach litter concentrations. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that tides play an especially important role, where an increasing tidal variability and tidal height leads to less litter found on beaches. Relatively straight and exposed coastlines appear to accumulate more litter. The regression model indicates that transport of litter through the marine environment is also important in explaining beach litter variability. By understanding which processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment, such as organizing beach cleanups during low tides at exposed coastlines. We estimate that 16 500–31 200 kg (95 % confidence interval) of litter is located along the 365 km of Dutch North Sea coastline

Using machine learning and beach cleanup data to explain litter quantities along the Dutch North Sea coast

Coastlines potentially harbor a large part of litter entering the oceans, such as plastic waste. The relative importance of the physical processes that influence the beaching of litter is still relatively unknown. Here, we investigate the beaching of litter by analyzing a data set of litter gathered along the Dutch North Sea coast during extensive beach cleanup efforts between the years 2014 and 2019. This data set is unique in the sense that data are gathered consistently over various years by many volunteers (a total of 14 000) on beaches that are quite similar in substrate (sandy). This makes the data set valuable to identify which environmental variables play an important role in the beaching process and to explore the variability of beach litter concentrations. We investigate this by fitting a random forest machine learning regression model to the observed litter concentrations. We find that tides play an especially important role, where an increasing tidal variability and tidal height leads to less litter found on beaches. Relatively straight and exposed coastlines appear to accumulate more litter. The regression model indicates that transport of litter through the marine environment is also important in explaining beach litter variability. By understanding which processes cause the accumulation of litter on the coast, recommendations can be given for more effective removal of litter from the marine environment, such as organizing beach cleanups during low tides at exposed coastlines. We estimate that 16 500–31 200 kg (95 % confidence interval) of litter is located along the 365 km of Dutch North Sea coastline

Macroplastic deposition and flushing in the Meuse river following the July 2021 European floods

Rivers play an important role in the macroplastics transport and retention dynamics in the environment. Therefore, gaining an understanding of these processes is vital in order to help determine the fate of macroplastic in the environment. During flood events the transport of floating macroplastic is known to increase. We compared plastic accumulation and flushing on sixteen riverbanks along the Dutch Meuse river during the period that includes the July 2021 extreme flood to plastic accumulation on the same riverbanks as during normal discharge conditions between 2018 and 2021. Here we show that following the flood small macroplastic categories ( $\lt\!2.5$ cm) were flushed from the riverbanks, while larger soft plastic fragments (2.5–50 cm) and wet tissues were deposited. We found that for all plastic categories and for all riverbanks averaged, the plastic accumulation rate during the July 2021 flood was higher than that which would be expected for that time of year. However, the average accumulation rate for all locations during the period that included the flood was lower than the average accumulation rate over the Dutch winter (Oct/Nov–Feb/Mar). Our results show that litter category composition following the July 2021 flood differed from normal conditions. This indicates that plastic deposition and remobilization processes on riverbanks differ between extreme flood and annually average conditions. These observations contribute to a better understanding of the fate of macroplastic in the environment in terms of the drivers of both its transport and deposition