Living and Working in a Healthy Environment: How Sensor Research in Flanders can Help Measure and Monitor Exposure to Certain Environmental Factors

People's daily living environment has an important influence on their physical and mental health. That living environment consists of many different components, as it is both a spatial or physical environment, and the result of many other processes (socio-cultural, economic context and individual characteristics and lifestyles). Overall, the pressure on the physical environment is very high, especially in densely populated and highly urbanised area’s such as Flanders, the northern part of Belgium. In urban environments, for instance, many spatial demands come together (space for housing, economy, mobility, green and blue infrastructures, etc.). The spatial layout of our cities can influence our health (e.g. whether or not we live nearby green spaces or in an environment that promotes active mobility, social contacts, if there are sources that impact the air quality, etc.), and of course our behaviour. The relation between health, living and working environment and spatial planning is complex. Therefore, the Flemish Department of Environment & Spatial Development has prepared a framework in 2019 to better capture that complex relationship, which we will briefly discuss in this paper. Broadly speaking, a policy committed to healthy environments may choose to make interventions that protect people's health from certain external factors (e.g. air pollution or environmental noise) or that enable and promote healthy lifestyles (e.g. physical activity, food,…). Next to that, providing citizens with up to date information is an important task of the government. In this paper, we discuss the research that the Environment and Health research team at the Flemish Department of Environment & Spatial Development conducts in order to measure human exposure to certain factors via sensors. Those particular factors were chosen mainly because they are part of themes around which the Flemish Department can make policy. We will consider three ongoing cases: measuring the quality of the indoor environment in different types of semi-public locations (such as schools, residential care centres, cultural centres,…), measuring radiofrequency radiation from fixed transmitting antennas in urban environments and measuring noise pollution. Partnering with international research & development organizations such as IMEC (Interuniversity Microelectronics Centre) and VITO (Flemish Institute for Technological Research), they supplied us with innovative and high-quality sensor technology. The sensors can transmit their measurement data in real time and participating parties can track the data on dashboards allowing immediate feedback and action when necessary. The results are intended to feed further research. Although not all case studies are equally advanced, we will conclude each one with possible policy actions

Methods for Regrouping Economic Activities into Meaningful Clusters

“The Flemish territory is characterized by a large urban sprawl […]. Even last years, an additional 6 hectares of undeveloped space is being built on daily. As a consequence open space is highly fragmented in Flanders“ (Pennincx, De Maeyer, Leroy, & De Mulder, 2021). As a strategic objective, the Flemish spatial government aims at a transition towards a net zero landtake daily by 2040. In this context, our spatial economy research group takes the choices and behaviour of individual companies and their use of space as a starting point. The main goal of the research is informing policy and supporting decision making by discerning spatial patterns, related to economic locations, and more precisely by focusing on the spatial environment of these locations. Over the years, we developed a the business-oriented approach for local spatial-economic policy and location advice for companies (Giaretta, Zaman, Pennincx, & De Mulder, 2019; Zaman, Pennincx, & De Mulder, 2020). For this, we need the exact location of the activity and the exact activity of every economic site. However,this information is difficult to gather from the only area-wide economic administrative database for the whole territory of Flanders (VKBO) (Gruijthuijsen et al., 2018). This area-covering database is used for major spatial-economic analyses, but it falls short in precision at the detail level needed for our work. We have carried out quite a lot of research in recent years to get to know the terrain situation by creating a field inventory. A key element of the research is the search for the right spatial synthesis of the data collected at the level of the parcel: through economic ecotopes and market segments we sought to combine the (economic) parcels into meaningful groups with similar characteristics. We described this step in previous papers (Giaretta et al., 2019; Zaman et al., 2020). Although the past research is interesting for the local policy makers of the mapped area’s, we still need to find a way to also make meaningful statements on spatial economic patterns for other areas in Flanders that have not been mapped. Producing this area-covering map for Flanders is rather important, as it will enable us to translate the analyses and the knowlegde we have gathered to (regional) policy. Although being thourough and rather precise, the visual inventory method has some drawbacks: it is time consuming and at this point, it cannot be easily applied to the entire area of Flanders. We therefore opt to first assess if we can extract useful statements regarding economic patterns from administrative databases. The main research question is whether the synthesis of the mapping data into the economic ecosystems or economic segments can be reproduced with the administrative database. Obviously, the results from the administrative database and the inventory will not be 100% alike. However, we believe it is possible come to spatial economic meaningful groups, even using the administrative database. The purpose of this grouping remains the same as with the inventory work and economic ecotopes and segments: being able to inform policy choices related to economic locations. In a first step, we examined whether and how the area synthesis (starting from the inventory and resulting into economic ecotopes and segments), that was carried out with manual work, field knowledge and expert opinion can be reproduced through automated methods, specifically through (1) statistical approach and/or machine learning and (2) a spatial predefined spatial clustering. The automated grouping results are reviewed and spatially analysed by spatial planners with territory knowledge. Only in a second step, when the grouping results on basis of the inventory are satisfying, we will rerun the method with the administrative data of the VKBO. In this paper we will discuss the first few steps of the grouping methods, in particular the distance and the activities clustering. We will outline the next steps, using the VKBO-data, assessing if we can come to meaningful economic clusters

Development and External Validation of Nomograms To Predict Adverse Pathological Characteristics After Robotic Prostatectomy: Results of a Prospective, Multi-institutional, Nationwide series

BACKGROUND: The possibility of predicting pathologic features before surgery can support clinicians in selecting the best treatment strategy for their patients. We sought to develop and externally validate pretreatment nomograms for the prediction of pathological features from a prospective multicentre series of robotic-assisted laparoscopic prostatectomy (RALP) procedures. DESIGN, SETTING, AND PARTICIPANTS: Between 2009 and 2016, data from 6823 patients undergoing RALP in 25 academic and community hospitals were prospectively collected by the Belgian Cancer Registry. Logistic regression models were applied to predict extraprostatic extension (EPE; pT3a,b-4), seminal vesicle invasion (SVI; pT3b), and high-grade locally advanced disease (HGLA; pT3b-4 and Gleason score [GS] 8-10) using the following preoperative covariates: prostate-specific antigen, clinical T stage, biopsy GS, and percentage of positive biopsy cores. Internal and external validation was performed. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The stability of the model was assessed via tenfold cross-validation using 80% of the cohort. The nomograms were independently externally validated using the test cohort. The discriminative accuracy of the nomograms was quantified as the area under the receiver operating characteristic curve and graphically represented using calibration plots. RESULTS AND LIMITATION: The nomograms predicting EPE, SVI, HGLA showed discriminative accuracy of 77%, 82%, and 88%, respectively. Following external validation, the accuracy remained stable. The prediction models showed excellent calibration properties. CONCLUSIONS: We developed and externally validated multi-institutional nomograms to predict pathologic features after RALP. These nomograms can be implemented in the clinical setting or patient selection in clinical trials. PATIENT SUMMARY: We developed novel nomograms using nationwide data to predict postoperative pathologic features and lethal prostate cancer.status: publishe

Development and external validation of nomograms to predict adverse pathological characteristics after robotic prostatectomy : results of a prospective, multi-institutional, nationwide series

Background: The possibility of predicting pathologic features before surgery can support clinicians in selecting the best treatment strategy for their patients. We sought to develop and externally validate pretreatment nomograms for the prediction of pathological features from a prospective multicentre series of robotic-assisted laparoscopic prostatectomy (RALP) procedures. Design, setting, and participants: Between 2009 and 2016, data from 6823 patients undergoing RALP in 25 academic and community hospitals were prospectively collected by the Belgian Cancer Registry. Logistic regression models were applied to predict extraprostatic extension (EPE; pT3a,b-4), seminal vesicle invasion (SVI; pT3b), and high-grade locally advanced disease (HGLA; pT3b-4 and Gleason score [GS] 8-10) using the following preoperative covariates: prostate-specific antigen, clinical T stage, biopsy GS, and percentage of positive biopsy cores. Internal and external validation was performed. Outcome measurements and statistical analysis: The stability of the model was assessed via tenfold cross-validation using 80% of the cohort. The nomograms were independently externally validated using the test cohort. The discriminative accuracy of the nomograms was quantified as the area under the receiver operating characteristic curve and graphically represented using calibration plots. Result and limitation: The nomograms predicting EPE, SVI, HGLA showed discriminative accuracy of 77%, 82%, and 88%, respectively. Following external validation, the accuracy remained stable. The prediction models showed excellent calibration properties. Conclusion:We developed and externally validated multi-institutional nomograms to predict pathologic features after RALP. These nomograms can be implemented in the clinical setting or patient selection in clinical trials. Patient summary: We developed novel nomograms using nationwide data to predict postoperative pathologic features and lethal prostate cancer. (C) 2018 Published by Elsevier B.V. on behalf of European Association of Urology