Background Variations in childbirth interventions may indicate inappropriate use. Most variation studies are limited by the lack of adjustments for maternal characteristics and do not investigate variations in adverse outcomes. This study aims to explore regional variations in the Netherlands and their correlations with referral rates, birthplace, interventions, and adverse outcomes, adjusted for maternal characteristics. Methods In this nationwide retrospective cohort study, using a national data register, intervention rates were analysed between twelve regions among single childbirths after 37 weeks’ gestation in 2010–2013 (n = 614,730). These were adjusted for maternal characteristics using multivariable logistic regression. Primary outcomes were intrapartum referral, birthplace, and interventions used in midwife- and obstetrician-led care. Correlations both between primary outcomes and between adverse outcomes were calculated with Spearman’s rank correlations. Findings Intrapartum referral rates varied between 55–68% (nulliparous) and 20–32% (multiparous women), with a negative correlation with receiving midwife-led care at the onset of labour in two-thirds of the regions. Regions with higher referral rates had higher rates of severe postpartum haemorrhages. Rates of home birth varied between 6–16% (nulliparous) and 16–31% (multiparous), and was negatively correlated with episiotomy and postpartum oxytocin rates. Among midwife-led births, episiotomy rates varied between 14–42% (nulliparous) and 3–13% (multiparous) and in obstetrician-led births from 46–67% and 14–28% respectively. Rates of postpartum oxytocin varied between 59–88% (nulliparous) and 50–85% (multiparous) and artificial rupture of membranes between 43–52% and 54–61% respectively. A north-south gradient was visible with regard to birthplace, episiotomy, and oxytocin. Conclusions Our study suggests that attitudes towards interventions vary, independent of maternal characteristics. Care providers and policy makers need to be aware of reducing unwarranted variation in birthplace, episiotomy and the postpartum use of oxytocin. Further research is needed to identify explanations and explore ways to reduce unwarranted intervention rates.
As the first order of business in the RIGHT project, each region produced and published its own regional report, using an underlying format developed in work package 3 in this project (Manickam & van Lieshout, 2018). The format and the regional work consisted of three parts. Part 1 is the Regional Innovation Ecosystems (RIE) mapping to provide a qualitative understanding of the region’s innovation ecosystem with regards to its Smart Specialisation Strategies (S3). This part is divided into a socio-economic and R&D profile mapping and a SWOT analysis. The RIE is an adaptation of a methodology and tool used by the eDIGIREGION Project. This part is to be filled in by desk research and consulting regional experts (through interviews and/or focus groups). This part is used for mapping the own regional ecosystems, information for the partners to get to know the other regions and to be able to identify relevant similarities and differences across the regions, which in turn, will be reported in part 1 of this trans-regional report. Regions themselves chose their own sector focus. One could focus on either energy of the blue sector, or both. Part 2 focuses on the innovation capacity and needs of SMEs from the chosen sector(s). The questions are adapted from a systemic study on cluster developments, in which an analysis model was developed (Manickam, 2018). It is based on (on average) six face-to-face interviews with SMEs from the sector. The outputs of these interviews were summarised into one template, in English, by each partner region to allow for joint analysis and comparison that is in turn reported in part 2 of this report Part 3 introduced the Job Forecasting and Skills Gaps mapping using the JOES templates as developed by van Lieshout et al. (2017). To gain an appreciation of the extent and nature of skills gap, each region was asked to analyse current and potential future labour demand, workforce, and discrepancies between the two, in up to 2 businesses. For obvious reasons (confidentiality and privacy), the JOEs will not be published separately, nor will their information be used in the report in a way that would be traceable to specific businesses. We will use exemplary information from them for illustrative purposes in Parts 1 and 2 of this report where relevant.
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Within PREMIUM_EU we have co-responsibility for developing the Regional Development Effects Module (RDEM). This module will map the impact of migration on regional development seen on different variables. To construct the RDEM we have to:1. develop a typology of regions, based on the impact that mobility has on its economic, social and cultural development; and2. detect the causal linkages between regional mobility on the one hand and regional development on the other.In our presentation we will focus on the process to determine relevant regional development indicators that will help in the collection and analysis of relevant data for the period 2010-2022 on NUTS 2 and 3 level. Partners in our project will additionally focus on:1. Analysis of regional networks estimated from Facebook2. Building typology regional development3. Longitudinal causal analysis of mobility4. Integration of case studiesFinally, this will result in:• Online atlas of mobility & development typologies• Report Causal Analysis of mobility development
