Lianne Zondag

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The midwife as influencing factor in clinical decision-making Exploration of the relationship between personal and professional factors and midwives’ clinical decisions about childbirth interventions Proefschrift voor het behalen van de graad van Doctor aan de Universiteit Maastricht, onder gezag van Rector Magnificus, Prof. dr. Pamela Habibović, overeenkomstig met het besluit van het College van Decanen, te verdedigen in het openbaar op donderdag 9 oktober 2025 om 10.00 uur door Dirkje Cornelia Zondag-McDermott Geboren op 26 augustus 1989 te Tiel

Promotor Prof. dr. M.J. Nieuwenhuijze (Universiteit Maastricht) Co-promotores Dr. T.M. van Haaren-ten Haken (Zuyd Hogeschool) Dr. P.M. Offerhaus (Zuyd Hogeschool) Beoordelingscommissie Prof. dr. J.D. de Jong, voorzitter (Universiteit Maastricht) Dr. J. van Dillen (Radboud Universiteit Nijmegen) Dr. J. T. Gitsels-van der Wal (Amsterdam UMC) Prof. dr. G.D.E.M. van der Weijden (Universiteit Maastricht) The work for this thesis was partly funded by Zuyd Hogeschool (Zuyd University of Applied Sciences) and the Royal Dutch Organization of Midwives (KNOV Scholarship 2023).

Contents Chapter 1 General introduction 7 Chapter 2 Regional variations in childbirth interventions in the Netherlands: a nationwide explorative study 21 Chapter 3 Regional variations in childbirth interventions and their correlations with adverse outcomes, birthplace and care provider: a nationwide explorative study 63 Chapter 4 Experiences, beliefs, and values influencing midwives’ attitude towards the use of childbirth interventions 95 Chapter 5 Knowledge and skills used for clinical decision-making on childbirth interventions: a qualitative study among midwives in the Netherlands 115 Chapter 6 Validation of the Birth Beliefs Scale for maternity care professionals in the Netherlands 135 Chapter 7 Practice variation in induction of labor: a critical document analysis on the contribution of regional protocols 151 Chapter 8 General discussion 173 Chapter 9 Summary 189 Chapter 10 Samenvatting 197 Appendices Impact paragraph 209 Curriculum vitae 246 Dankwoord 215

1 General introduction

8 Chapter 1 Madeline* – a case study Madeline is a client in our practice. A few weeks ago, she phoned to tell that she hadn’t been able to sleep that night, because of irregular contractions. Madeline was 40 weeks pregnant with her third child and had given birth twice at home in a birthing pool. She asked if it was possible to get a check-up because she would like to know if she already had some cervical dilatation or not. If possible, she would like to have her membranes swept in the hope that labour would start. On checking, she was two centimetres dilated, the baby was in good condition, and her membranes were swept as she had requested. An hour or three later, Madeline called that her contractions came every 5 minutes. I visited Madeline at home and she was six centimetres dilatated. However, I was not really impressed with the contractions. They were irregular, and above all, they appeared not very strong. Madeline was very relaxed and chatted with everyone in the room. Of one thing she was determined, she wanted to give birth tonight and she was ready to jump into the birthing pool. But what was I going to do? Was I facing ineffective uterine contractions? Should I rupture the membranes or go to the hospital? Should I prepare for an increased risk of severe blood loss? What do our regional protocols say about this situation, and who is the obstetrician on call tonight? Or was this situation still physiological and should I just be present and wait for stronger contractions? *Madeline is a fictitious name and this case study has been edited to hide her identity. Practice variation in maternity care Clinical variation in interventions, such as diagnoses, treatments, and drug prescriptions, appears in a wide range of acute and chronic care specialities, both in primary care and in hospital settings. A systematic review of variation in medical practice variation in OECD countries, including 836 published studies, described variation between regions and hospitals, with differences in physician practices for same conditions (1). Practice variation in maternity care has also been described, with variation in rates of caesarean section and induction of labour as being the most commonly reported (2). Remarkably is the variation of the same interventions between different countries. In 2017, for example, 21.4% of all births in the Netherlands were induced, while America had a rate of induction of labour of 25.7% and Australia 33.0% (3–5).

9 General introduction With the aim of gaining an understanding of variation in childbirth interventions, research has focused on increasing rates of interventions and variation between countries (1–5). Beside variation between countries, there also appears to be variation in childbirth interventions within countries. Practice variation in Dutch maternity care was described in 2014 based on data from the national perinatal register (6). Analysis of these data showed that the rates of interventions, such as caesarean section, instrumental birth, and induction of labour varied widely between different hospitals. For example, there was little variation between hospitals in the rates of planned caesarean sections, but there was much variation in the rates of unplanned caesarean sections. Hospitals with a low rate of unplanned caesarean sections had a rate of around 4.8%, while the highest rates were around 21%. A similar variation was found between hospitals for induction of labour. Practice variation has also been described in primary midwifery practices, with variations in intrapartum referral rates from 9.7% to 63.7% (7). Warranted versus unwarranted variation Interventions in childbirth are in certain circumstances useful to prevent perinatal morbidity and mortality (1). Some practice variation in childbirth interventions is to be expected as care is adapted to medical conditions or the woman’s preferences. However, large variations in care of fairly homogenous populations may indicate insufficient quality of care. Underuse of interventions can lead to preventable morbidity and mortality, while overuse of interventions during maternity care can result in medicalisation of physiological pregnancy and childbirth (1,2). The use of unnecessary interventions can harm women - physically and psychologically - and their newborns, and increase health care costs (3). Variations that reflect under- or overuse of interventions may be unwarranted. Several researchers have tried to define warranted and unwarranted variation (8–10). Sutherland and Levesque have designed an analytical framework that identifies elements associated with warranted and unwarranted variation (8). These elements can be used to assess whether the variation is warranted or unwarranted and include the categories: evidence, capacity, and agency. These elements are interrelated and highly sensitive to context, such as patients living in lower socio-economic status areas are often reported to have worse outcomes than patients living in higher socio-economic status areas. This makes assessment difficult and requires nuance and reflexivity. Because the elements cannot be separated from the context, warranted and unwarranted variation cannot be explained by only using quantitative data sets (8). Causes of unwarranted variation are 1) lack of evidence-based care (evidence); 2) differences in the availability of healthcare resources (capacity); and/or 3) care providers offering care based on the beliefs and personal interests (agency) (8). In other words, when variation cannot be explained by medical 1

10 Chapter 1 conditions, population characteristics or patient preferences and occurs despite strong evidence-based recommendations, it is defined as unwarranted (9,11). Unwarranted variation is a problem because it contributes to inequalities in the availability and use of health services. As a result, interventions in childbirth may be used inappropriately, with interventions used too little and too late on the one hand, and interventions used too much and too soon on the other hand (1). Explaining practice variation Practice variation can be explained by a sociological model that describes factors that interact with practice variation at macro-, meso-, and micro-level (12) (Figure 1). Figure 1. Theoretical model for the explanation of medical practice variation (de Jong et al, 2015) Although the model describes mechanisms on different levels, these levels are not isolated but are interrelated with each other. Organisational arrangements at meso-level can, for example, influence individual choices made at microlevel. We will provide further insight into practice variation in maternity care by describing the factors at the different levels of the sociological framework. Within this description we will also link elements of Sutherland’s analytical framework about warranted and unwarranted practice variation (8). Practice variation in maternity care: macro-level The organisation of maternity care in the Netherlands is based on the principle that pregnancy, childbirth, and postpartum care are physiological processes. If risk factors are present, they are identified in time to be monitored and to prevent or treat the pathology they may cause (13). Emphasis is placed on early prevention. Uncomplicated pregnancies of healthy women are cared for by community midwives. Community midwives are autonomous

11 General introduction health professionals, usually working in independent practices. They attend home births, uncomplicated hospital births and births in birth centres, and are capable to make autonomous decisions together with the woman about childbirth interventions or the intervention of referral to obstetrician-led care (14). Indications for referral from midwife-led to obstetrician-led care are described in the obstetric indication list of 2003 and multidisciplinary guidelines (15). Midwives are allowed to perform certain interventions, such as artificial rupture of membranes or episiotomies, while other interventions are restricted to obstetrician-led care. There hospital-based midwives and obstetricians provide care for women with specific risk factors or complications and more childbirth interventions are available, such as augmentation of labour, analgesia, and instrumental birth (14). The organisational context can be the basis of practice variation through organisational structures, regulations, population characteristics or resource constraints that influence clinical decision-making (8). The organisation of maternity care in the Netherlands differs from other high-income countries because of the division between midwife-led and obstetrician-led care. This may contribute to practice variation at the macro level: the high rate of home births in the Netherlands compared to rates in other countries (16). In this thesis, we focus on mechanisms that can help explain practice variation at the meso and micro level. We will therefore explain the mechanisms at these levels in more detail. Practice variation in maternity care: meso-level In the Netherlands, community midwives, hospital-based midwives, obstetricians, and other disciplines such as paediatricians and maternity care assistants collaborate regionally in maternity care networks (MCNs) (17,18). An MCN is usually situated around one hospital and the midwifery practices in the same region. The number of professionals involved varies from about 30 to 120, depending on the number of births and the level of urbanisation in the region. Professionals in an MCN are collectively responsible for the quality of maternity care in that region and are expected to continually evaluate perinatal outcomes and women’s experiences in order to improve the quality and efficiency of their care (18). Collaboration in MCNs has intensified over time and has stimulated the development of regional protocols within the networks (18). In general, protocols are more context-specific and describe the ‘who’, and ‘how’ of medical practice provided in a given region, while national guidelines describe the ‘what’ and ‘when’ based on available evidence. The quality of collaboration between the different disciplines in an MCN is an important issue in relation to practice variation at the meso-level. Good collaboration encourages to make joint agreements and to reflect on care between healthcare professionals in the same midwifery practice or MCN (8,19). However, multidisciplinary collaboration also comes with challenges. 1

12 Chapter 1 Regional care should be designed as integrated care for pregnant women, respecting patient preferences and the specific expertise and autonomy of different healthcare professionals (13). Collaboration in MCNs can be challenging because professionals with different expertise and paradigms need to align (20). Particularly between the disciplines of community midwives and obstetricians, there are examples of each discipline having concerns about the other’s professional perspective on birth and its impact on birth outcomes. Midwives have concerns about the medicalisation of childbirth, while obstetricians have expressed concerns that an overemphasis on physiological childbirth might overlook risks to mothers and their newborns (21). Another challenge is to ensure equality between the different disciplines. Relationships between midwives and obstetricians seem to be influenced by the history of maternity care. Historically, the midwifery profession has been predominantly female and has fewer years of training than obstetricians (22). This hierarchy is also reflected in the experienced collaboration between midwives and obstetricians in MCNs. Midwives perceive an imbalance of power in their professional relationship with obstetricians and are cautious about collaborating with obstetricians (23–25). International research has shown that the culture of maternity units has an influence on the intervention rates (26–28). Individual professionals working in the same maternity unit have comparable intervention rates, but the intervention rates between maternity units differ. Evidence suggests that intervention rates in Dutch maternity care are influenced by the culture of a midwifery practice or an MCN (7). Healthcare professionals’ birth beliefs are a factor that contributes to attitudes and clinical decisions about interventions, and are therefore part of the culture (29). However, it is unclear how these beliefs influence clinical decision making on interventions. Hospital protocols in other disciplines have shown that hospital culture is reflected in recommendations for treatment or interventions (19,30). It is possible that a similar mechanism applies to maternity care, with regional culture reflected in regional protocols formulated by MCNs. Practice variation in maternity care: micro-level The third level of the sociological model on practice variation - the micro level - describes the interaction between the woman and the maternity care professional to achieve individual decision-making (12). At this level, the professional applies their theoretical knowledge and professional experience to individual situations. Preferably, the values and preferences of the individual woman are explored through shared decision-making, creating a conversation about clinical characteristics and woman’s preferences. Decision-making at the micro-level appears to be influenced by the attitude of the healthcare professional (29). To study attitudes, intentions, and other factors that influence behaviour theories on human behaviour are used. Underlying reasons why

13 General introduction healthcare professionals show certain behaviour can be explored with the Attitude, Social Norms, Self-efficacy (ASE) model (31–34). According to the ASE-model, individual experiences, beliefs, and values influence a person’s attitude, which subsequently, together with social norms and self-efficacy, shapes the intention to perform a specific behaviour (Figure 2). Factors described as ‘knowledge and skills’ and ‘barriers and facilitators’ interact with a person’s intentions until they translate in actual behaviour, such as the use of an intervention. For example, organisational factors in the professional environment, such as workload and volume targets, influence individual decision-making (behaviour) (19). In this example, we see that factors at the meso-level (professional environment) influence the micro-level (individual healthcare professional). A closer look at the midwife’s role as a healthcare professional in decisionmaking shows the influence of factors such as workload, setting (home or hospital), and regional protocols (35–37). In addition, more individual factors based on experience, beliefs, and values influence a midwife’s decisionmaking. Their attitudes to physiology, woman-centredness, and shared decision-making, as well as individual risk perception, have an influence (35,36). A midwife’s risk perception appears to be influenced by the social or medical model (38,39). Midwives who believe that pregnancy and birth are largely normal and healthy processes, think according to the social model. This model is characterised by the involvement of the pregnant woman, a holistic approach, and the social context. In contrast, the medical model is characterised by more monitoring, risk-thinking, and higher rates of interventions. Figure 2. The ASE-model (33) 1

14 Chapter 1 As individual decision-making in maternity care is achieved through interaction between the woman and the midwife, the woman is also a factor influencing practice variation (36,40). Woman’s values and preferences influence the decision to perform interventions, and therefore influence with the clinical decisions of a midwife. Research on shared decision-making found that the introduction of shared decision-making increases the amount of different treatments. In other words, through the interaction with the woman variation within hospitals increased (41). In the Netherlands, community midwives are professionals who make autonomous decisions about childbirth interventions or referrals to obstetrician-led care (13). Although Dutch midwifery educational programmes are similar and based on physiology, there is a wide variation in intrapartum referral rates between midwives and midwifery practices (7). Similar to previous research among physicians, it is likely that midwives’ beliefs about treatment and clinical decisions also vary widely (42). However, it is not known how midwives’ beliefs and other personal and professional factors influence clinical decision-making when deciding on the use of interventions or referrals. Problem statement and knowledge gaps Internationally, the interventions used in childbirth vary widely, and there is evidence to suggest that this applies to the Netherlands. Practice variation can be an indicator of unwarranted variation, which can lead to avoidable harm, inequalities in quality of care, and high costs. Regional variation in the Netherlands has not been studied extensively. Possible patterns in intervention rates can provide useful insights, for example whether all intervention rates are higher in certain regions or whether it is a selection of intervention rates that are higher or lower. Therefore, correlations between intervention rates in different regions, adjusted for population characteristics, should be investigated to better understand practice variation in the Netherlands. Different mechanisms that influence practice variation can be understood by the sociological model of de Jong et al (12). To our knowledge, this model has not been used in maternity care. By using this model, we expect to gain knowledge about practice variation in maternity care at meso- and micro-level. As practice variation is a large topic, not all factors can be studied. For this thesis, we focused on the role of midwives and their clinical decisions about the use of interventions. There are indications that the use of interventions varies between midwives and we want to explore what causes this variation. Therefore, we explored how midwives’ personal and professional factors influence their clinical decisions about interventions in childbirth and consequently influence practice variation in maternity care.

15 General introduction General aim and research questions The general aim of this thesis is to generate more knowledge about how midwives’ personal and professional factors are related to their clinical decisions about childbirth interventions. This can contribute to reducing unwarranted practice variation in maternity care. The first research question aims to provide more background knowledge about the current regional variation within the Netherlands, after which the other research questions focus on the general aim. The findings of this thesis focus on two different levels for practising midwives: 1) the individual midwife (micro-level) 2) the midwife as part of the collaboration within midwifery practices or maternity care networks (meso-level). On the level of the microlevel, we want to explore how midwives’ attitude, knowledge, and skills influence clinical decisions on the use of interventions. On the meso-level, we want to explore if differences in birth beliefs of maternity care professionals and regional protocols are a factor contributing to practice variation. The following research questions are addressed in this thesis: 1. Which regional variations in childbirth intervention rates exist in the Netherlands, and how are these variations associated to maternal and perinatal outcomes? (meso-level) 2. What experiences, beliefs, and values influences midwives’ attitudes toward childbirth interventions? (micro-level) 3. How do knowledge and skills influence clinical decision-making of midwives on the appropriate use of childbirth interventions? (micro-level) 4. Can the Birth Beliefs Scale be used to measure beliefs towards the nature of birth (medical or natural) among maternity care professionals? (micro and meso-level) 5. What is the variation in regional protocols with regard to recommendations on induction of labour, and do regional protocols contribute to practice variation? (meso-level) Outline of this thesis Following the general introduction to the thesis, chapter 2 describes the regional variation of commonly used childbirth interventions in obstetricianled care in the Netherlands, and how these variations were correlated both to each other and to maternal and perinatal outcomes, adjusted for population characteristics. (Q1) In chapter 3, regional variations and correlations in the Netherlands were also described, but in this chapter for childbirth interventions that are used in both midwife-led and obstetrician-led care. (Q1) 1

16 Chapter 1 Chapter 4 presents the findings of individual interviews with community midwives working in the Netherlands in order to explore different attitudes toward childbirth interventions. (Q2) Chapter 5 reports the outcomes of our follow-up study in which we explored how knowledge and skills influence midwives’ clinical decision-making on the appropriate use of childbirth interventions. (Q3) Chapter 6 describes the validation of the Birth Beliefs Scale for maternity care professionals. In addition, we have compared the birth beliefs of maternity care professionals working in different maternity care networks. (Q4) Chapter 7 provides the results of a document analysis on variation in regional protocols as one of the factors contributing to practice variations. (Q5) In chapter 8, we discuss the main findings of the studies contained within this thesis and we reflect on the methodological strengths and limitations of those studies. We interpret the findings in a broader perspective and discuss the implications for practice and future research.

17 General introduction References 1. Miller S, Abalos E, Chamillard M, Ciapponi A, Colaci D, Comandé D, et al. Beyond too little, too late and too much, too soon: a pathway towards evidence-based, respectful maternity care worldwide. Vol. 388, The Lancet. Lancet Publishing Group; 2016. p. 2176–92. 2. Brownlee S, Chalkidou K, Doust J, Elshaug AG, Glasziou P, Heath I, et al. Evidence for overuse of medical services around the world. Vol. 390, The Lancet. Lancet Publishing Group; 2017. p. 156–68. 3. Gibbons L, Belizán JM, Lauer JA, Betrán AP, Merialdi M, Althabe F. The Global Numbers and Costs of Additionally Needed and Unnecessary Caesarean Sections Performed per Year: Overuse as a Barrier to Universal Coverage World Health Report (2010) Background Paper, 30 HEALTH SYSTEMS FINANCING. 4. Christiaens W, Nieuwenhuijze MJ, de Vries R. Trends in the medicalisation of childbirth in Flanders and the Netherlands. Midwifery. 2013 Jan;29(1). 5. Mikolajczyk RT, Schmedt N, Zhang J, Lindemann C, Langner I, Garbe E. Regional variation in caesarean deliveries in Germany and its causes. BMC Pregnancy Childbirth. 2013 May 1;13:99. 6. RIVM. Zorgbalans 2014 De prestaties van de Nederlandse gezondheidszorg. 7. Offerhaus PM, Geerts C, de Jonge A, Hukkelhoven CWPM, Twisk JWR, LagroJanssen ALM. Variation in referrals to secondary obstetrician-led care among primary midwifery care practices in the Netherlands: A nationwide cohort study. BMC Pregnancy Childbirth. 2015 Feb 21;15(1). 8. Sutherland K, Levesque JF. Unwarranted clinical variation in health care: Definitions and proposal of an analytic framework. J Eval Clin Pract. 2020 Jun 1;26(3):687–96. 9. Wennberg JE. Time to tackle unwarranted variations in practice. BMJ. 2011 Mar 17;342:d1513. 10. Corallo AN, Croxford R, Goodman DC, Bryan EL, Srivastava D, Stukel TA. A systematic review of medical practice variation in OECD countries. Health Policy (New York). 2014 Jan;114(1):5–14. 11. Mercuri M, Gafni A. Medical practice variations: What the literature tells us (or does not) about what are warranted and unwarranted variations. J Eval Clin Pract. 2011 Aug;17(4):671–7. 12. de Jong JD, Groenewegen PP, Westert GP. Sociological Model for Understanding Medical Practice Variations. In: Medical Practice Variations. Springer US; 2015. p. 1–15. 13. Harmsen van der vliet - Torij H. Een goed begin is het halve werk - Verloskunde in de kern van preventie. Hogeschool Rotterdam Uitgeverij; 2017. 14. Amelink-Verburg MP, Buitendijk SE. Pregnancy and labour in the dutch maternity care system: What is normal? The role division between midwives and obstetricians. J Midwifery Womens Health. 2010 May;55(3):216–25. 15. College voor zorgverzekeringen. Verloskundig vademecum 2003. Diemen; 2003. 16. De Vries R, Buitendijk S. Science, safety, and the place of birth: Lessons from the Netherlands [Internet]. 2012. Available from: https://www.researchgate.net/ publication/267695078 1

18 Chapter 1 17. Boesveld IC, Valentijn PP, Hitzert M, Hermus MAA, Franx A, de Vries RG, et al. An Approach to measuring Integrated Care within a Maternity Care System: Experiences from the Maternity Care Network Study and the Dutch Birth Centre Study. Int J Integr Care. 2017 Jun 20;17(2):6. 18. College Perinatale Zorg. Zorgstandaard Integrale Geboortezorg Versie 1.2. Utrecht; 2020. 19. Atsma F, Elwyn G, Westert G. Understanding unwarranted variation in clinical practice: A focus on network effects, reflective medicine and learning health systems. International Journal for Quality in Health Care. 2020 May 1;32(4):271–4. 20. van Zijp C, van Urk F, Dreef S, de Graaf A, Verheul M. Samen (net)werken in de zorg: doorbreken van patronen - Leren van de geboortezorg: evaluatie-onderzoek naar (het ontstaan van) samenwerking in verloskundige samenwerkingsverbanden. Berenschot. 2021 May 19. 21. Keating A, Fleming VEM. Midwives’ experiences of facilitating normal birth in an obstetric-led unit: a feminist perspective. Midwifery. 2009 Oct;25(5):518–27. 22. Van Der Lee N, Driessen EW, Scheele F. How the past influences interprofessional collaboration between obstetricians and midwives in the Netherlands: Findings from a secondary analysis. J Interprof Care. 2016 Jan 2;30(1):71–6. 23. Cronie D, Rijnders M, Jans S, Verhoeven CJ, de Vries R. How good is collaboration between maternity service providers in the Netherlands? J Multidiscip Healthc. 2019;12:21–30. 24. Schölmerich VLN, Posthumus AG, Ghorashi H, Waelput AJM, Groenewegen P, Denktaş S. Improving interprofessional coordination in Dutch midwifery and obstetrics: A qualitative study. BMC Pregnancy Childbirth. 2014 Apr 15;14(1). 25. van der Lee N, Driessen EW, Scheele F. How the past influences interprofessional collaboration between obstetricians and midwives in the Netherlands: Findings from a secondary analysis. J Interprof Care. 2016;30(1):71–6. 26. Klein MC, Kaczorowski J, Hall WA, Fraser W, Liston RM, Eftekhary S, et al. The Attitudes of Canadian Maternity Care Practitioners Towards Labour and Birth: Many Differences but Important Similarities. Journal of Obstetrics and Gynaecology Canada. 2009;31(9):827–40. 27. White VanGompel E, Perez S, Wang C, Datta A, Cape V, Main E. Measuring labor and delivery unit culture and clinicians’ attitudes toward birth: Revision and validation of the Labor Culture Survey. Birth. 2019 Jun 1;46(2):300–10. 28. Mead MMP, Kornbrot D. The influence of maternity units’ intrapartum intervention rates and midwives’ risk perception for women suitable for midwifery-led care. Midwifery. 2004;20(1):61–71. 29. Coates D, Donnolley N, Henry A. The Attitudes and Beliefs of Australian Midwives and Obstetricians About Birth Options and Labor Interventions. J Midwifery Womens Health. 2021 Mar 1;66(2):161–73. 30. Balakrishnan M, Raghavan A, Suresh GK. Eliminating Undesirable Variation in Neonatal Practice: Balancing Standardization and Customization. Clin Perinatol. 2017 Sep;44(3):529–40. 31. Ajzen I. The Theory of Planned Behavior. Organ Behav Hum Decis Process. 1991;50:179–211.

19 General introduction 32. Ajzen I. The theory of planned behaviour: reactions and reflections. Psychol Health. 2011 Sep;26(9):1113–27. 33. De Vries H, Dijkstra M, Kuhlman P. Self-efficacy: the third factor besides attitude and subjective norm as a predictor of behavioural intentions. Health Educ Res [Internet]. 1988;3(3):273–82. Available from: https://academic.oup.com/her/ article-abstract/3/3/273/671516 34. Fishbein M, Ajzen I. Predicting and changing behavior: The Reasoned Action Approach. New York: Taylor & Francis. 2010. 35. Thompson SM, Nieuwenhuijze MJ, Low LK, de Vries R. Exploring Dutch midwives’ attitudes to promoting physiological childbirth: A qualitative study. Midwifery. 2016 Nov 1;42:67–73. 36. Daemers DOA, van Limbeek EBM, Wijnen HAA, Nieuwenhuijze MJ, de Vries RG. Factors influencing the clinical decision-making of midwives: A qualitative study. BMC Pregnancy Childbirth. 2017 Oct 6;17(1). 37. Weltens M, de Nooijer J, Nieuwenhuijze MJ. Influencing factors in midwives’ decision-making during childbirth: A qualitative study in the Netherlands. Women and Birth. 2019 Apr 1;32(2):e197–203. 38. MacKenzie Bryers H, van Teijlingen E. Risk, theory, social and medical models: A critical analysis of the concept of risk in maternity care. Midwifery. 2010 Oct;26(5):488–96. 39. Van Teijlingen E. A critical analysis of the medical model as used in the study of pregnancy and childbirth. In: Sociological Research Online. University of Surrey; 2005. 40. Brabers A (Adriana EM, Nederlands instituut voor onderzoek van de gezondheidszorg (Utrecht). Patient involvement and medical practice variation: can patients be ignored in theories about practice variation? 2018. 41. Brabers AEM, van Dijk L, Groenewegen PP, van Peperstraten AM, de Jong JD. Does a strategy to promote shared decision-making reduce medical practice variation in the choice of either single or double embryo transfer after in vitro fertilisation? A secondary analysis of a randomised controlled trial. BMJ Open. 2016 May 6;6(5):e010894. 42. Cutler D, Skinner JS, Stern AD, Wennberg D. Physician beliefs and patient preferences: A new look at regional variation in health care spending. Am Econ J Econ Policy. 2019 Feb 1;11(1):192–221. 1

2 Regional variations in childbirth interventions in the Netherlands: a nationwide explorative study Anna Seijmonsbergen-Schermers, Lianne Zondag, Marianne Nieuwenhuijze, Thomas van den Akker, Corine Verhoeven, Caroline Geerts, François Schellevis, Ank de Jonge BMC Pregnancy Childbirth. 2018 Jun 1;18(1):192 doi: 10.1186/s12884-018-1795-0

22 Chapter 2 Abstract Background Although interventions in childbirth are important in order to prevent neonatal and maternal morbidity and mortality, non-indicated use may cause avoidable harm. Regional variations in intervention rates, which cannot be explained by maternal characteristics, may indicate over- and underuse. The aim of this study is to explore regional variations in childbirth interventions in the Netherlands and their associations with interventions and adverse outcomes, controlled for maternal characteristics. Methods Childbirth intervention rates were compared between twelve Dutch regions, using data from the national perinatal birth register for 2010-2013. All single childbirths from 37 weeks’ gestation onwards were included. Primary outcomes were induction and augmentation of labour, pain medication, instrumental birth, caesarean section (prelabour, intrapartum) and paediatric involvement. Secondary outcomes were adverse neonatal and maternal outcomes. Multivariable logistic regression analyses were used to adjust for maternal characteristics. Associations were expressed in Spearman’s rank correlation coefficients. Results Most variation was found for type of pain medication and paediatric involvement. Epidural analgesia rates varied from between 12-38% (nulliparous) and from between 5-14% (multiparous women). These rates were negatively correlated with rates of other pharmacological pain relief, which varied from between 15-43% (nulliparous) and from between 10-27% (multiparous). Rates of paediatric involvement varied from between 37-60% (nulliparous) and from between 26-43% (multiparous). For instrumental vaginal births, rates varied from between 16-19% (nulliparous) and from between 3-4% (multiparous). For intrapartum caesarean section, the variation was 13-15% and 5-6%, respectively. A positive correlation was found between intervention rates in midwife-led and obstetrician-led care at the onset of labour within the same region. Adverse neonatal and maternal outcomes were not lower in regions with higher intervention rates. Higher augmentation of labour rates correlated with higher rates of severe postpartum haemorrhage. Conclusions Most variation was found for type of pain medication and paediatric involvement, and least for instrumental vaginal births and intrapartum caesarean sections. Care providers and policy makers should critically audit remarkable variations, since these may be unwarranted. Limited variation for some interventions may indicate consensus for their use. Further research should focus on variations in evidence-based interventions and indications for the use of interventions in childbirth.

23 Regional variations in childbirth interventions in the Netherlands Introduction The rates of interventions in childbirth vary worldwide (1-4) and have fluctuated over the years (1, 4-7). Induction of labour and caesarean section (CS) rates have shown a steady increase since the 1970s (1, 4, 6, 8, 9), which raised concerns (10). Interventions in childbirth are important in order to prevent neonatal and maternal morbidity and mortality. However, use without a medical indication may cause avoidable harm (2, 11-14). The World Health Organization (WHO) recommends limited use of interventions during childbirth (15). Induction and augmentation of labour should only be performed on medical indication (16, 17). However, there are concerns about poor adherence to this recommendation in a significant number of women with uncomplicated pregnancies (16-19). Epidural analgesia is the most effective method for pain medication during labour (20), but is associated with a higher risk of instrumental birth, oxytocin use, maternal fever, urinary retention and complications, such as post-dural puncture headache (20, 21). The decision for pain medication is ultimately based on women’s choice. There is some evidence that continuous support of labour might reduce the need for pain medication (22). Furthermore, the WHO states that CS rates higher than ten percent at population level are not associated with reductions in maternal, neonatal and infant mortality rates (23). Variations in intervention rates between high-income countries may be explained by culture and history, differences in population characteristics, maternity care systems, and national guidelines (12, 15, 24-26). Clinical guidelines have been used for a long time to harmonise and rationalise the use of interventions within countries, and to improve outcomes (27, 28). Nevertheless, studies comparing regions within countries like England, Ireland, Canada and Germany, have found substantial variations in rates of induction of labour, epidural analgesia, continuous fetal electronic monitoring, episiotomy, instrumental birth, and CS (29-33). Additionally, Dutch studies have reported variations in rates between hospitals, of induction and augmentation of labour, administration of sedation and analgesics, episiotomy, instrumental birth, and CS (34, 35). Regional variations in intervention rates, which cannot be explained by maternal characteristics, may indicate over- and underuse (36). This is especially true in a relatively small country without regional differences in the maternity healthcare system. The aim of this study was therefore to explore which regional variations in intervention rates in childbirth exist, and how these variations are associated both to each other, and to adverse neonatal and maternal outcomes. These are explored for single childbirths from 37 weeks of gestation onwards in midwife- or obstetrician-led care in the Netherlands, and controlled for maternal characteristics. 2

24 Chapter 2 Methods Data collection For this nationwide study, we used consolidated data of the years 2010 to 2013 from Perined, the national perinatal register that includes data from almost all births in the Netherlands. Perined aims to improve the quality of perinatal care through providing data for research and audits on adverse outcomes. The Perined register includes data from: primary midwife-led care (the national perinatal database 1); secondary obstetrician-led care (the national perinatal database 2); paediatric care (the national neonatal register); and primary midwifery care by general practitioners (the national perinatal database h). The data are routinely recorded by the care providers and combined into the Perined register via a validated linkage method (37, 38). More than 98% of all midwifery practices and obstetric hospital units record their births in this combined database (39). All single childbirths from 37 weeks’ gestation onwards were included. Exclusion criteria were missing data on: postal code; parity; or from the national perinatal database 1, covering midwife-led care, but where the woman was referred to obstetrician-led care, covered by the national database 2. In the Netherlands, low-risk women in primary midwife-led care are cared for by independent midwives who attend home births, low-risk hospital births, and births in alongside and free-standing birth centres. The Dutch Birth Centre Study showed that health outcomes, experiences, and costs for lowrisk women are similar for planned birth in a birth centre and planned birth in a hospital, both supervised by a primary care midwife (40, 41). When risks for adverse outcomes increase or complications arise, women are referred to obstetrician-led care. Interventions in childbirth such as induction and augmentation of labour, pain medication, instrumental birth, and CS, are only available in an obstetrician-led care setting (42, 43). Intrapartum interventions may be used for women in midwife-led care at the onset of labour after referral to obstetrician-led care. Therefore, intervention rates are not comparable for women who are in midwife-led care and women who are in obstetrician-led care at the onset of labour. The VU University Medical Center confirmed that ethical approval was not required for this study according to the Dutch legislation (reference WC2016055; http://www.ccmo.nl/en/your-research-does-it-fall-under-the-wmo). Interventions Births were attributed to one of the twelve Dutch administrative provinces (further referred to as ‘regions’) according to the residential postal code of the mother. All low-risk women have access to all types of birth settings, but not all types are present in all regions (44). We adjusted for this by using the residential postal code of the mother.

25 Regional variations in childbirth interventions in the Netherlands The following interventions were examined as the primary outcomes: induction of labour; augmentation after a spontaneous onset of labour; intrapartum oxytocin use; epidural analgesia; other pharmacological pain relief; instrumental vaginal birth; CS (prelabour, intrapartum); and involvement of a paediatrician in the first 24 hours after birth. Births from 42 weeks onwards were not excluded, because they may explain variation in particularly induction of labour rates, and they may reflect different policies between regions. Artificial rupture of membranes before a spontaneous onset of labour was defined as induction of labour, and administration of oxytocin to stimulate uterine contractions after spontaneously ruptured membranes as augmentation. A CS after spontaneously ruptured membranes was defined as intrapartum CS. Intrapartum oxytocin includes the use of oxytocin for induction or for augmentation of labour, but not oxytocin use in the third stage of labour. Women with a prelabour CS were excluded from the analyses on pain medication. Women with an intrapartum CS and an epidural, are classified as epidural analgesia for labour pain, since epidural analgesia is generally not used for caesarean sections without prior epidural analgesia for labour pain. In Perined ‘other pharmacological pain relief’ is specified as: sedatives; nonopioid analgesics; and opioid analgesics without further details. The most common opioid analgesics are pethidine injections, sometimes combined with a sedative such as promethazine, and patient-controlled remifentanil (45). In some births, epidural analgesia and other pharmacological methods for pain medication were both used, and therefore, the percentages could not be added up (45). Neonatal and maternal outcomes The secondary neonatal and maternal outcomes were: antepartum and intrapartum stillbirth; neonatal mortality; Apgar score below 7 at 5 minutes; third or fourth degree perineal tear among vaginal births; and postpartum haemorrhage (PPH) of 1000 ml or more. Antepartum stillbirths with births beyond 37 weeks were included, since this may influence intervention rates. Neonatal mortality was defined as neonatal death up to 7 days. Antepartum and intrapartum stillbirths were excluded from the analyses on Apgar score. Women who gave birth by CS were excluded from the analyses on third or fourth degree perineal tear. Maternal and neonatal characteristics The following maternal and neonatal characteristics were included as independent variables or potential confounders (29, 30, 32, 46-49): parity (nulliparous, multiparous); care setting at the onset of labour (midwife-led, obstetrician-led), maternal age (<20, 20-24, 25-29, 30-34, 35-39, ≥40 years); ethnic background (Dutch, non-Dutch); degree of urbanisation (urban, intermediate, rural); socioeconomic status (high, medium, low); gestational age (37+0 - 37+6, 38+0 - 40+6, 41+0 - 41+6, ≥42 weeks); and birth weight (<2.3rd, <10th, >90th, >97.7th percentile). Ethnic background was reported by the care provider and was defined as Dutch or non-Dutch, because of 2

26 Chapter 2 inconsistencies in recording non-Dutch subgroups. The degree of urbanisation was based on the four digits of the residential postal code of the mother. For 2,500 or more addresses/km2, the degree of urbanisation was categorized as urban, and for less than 500 addresses/km2 as rural. Socioeconomic status [SES] was based on a proxy measure indicated by the Netherlands Institute for Social Research (SCP), which includes education, employment, and level of income of the residential postal code area (Statistics Netherlands; https:// bronnen.zorggegevens.nl/Bron?naam=Sociaal-Economische-Status-perpostcodegebied). SES was classified as high, medium and low, based on the 25 and 75 percentile cut-off points. Data analysis The baseline characteristics were described in percentages per region. The variation in interventions was analysed overall, and in subgroups according to the care setting. Stratification by parity was applied for the crude rates. Univariable analyses were performed to gain insight in the variations of intervention rates and childbirth outcomes in the twelve regions. All interventions and childbirth outcomes mentioned above were included in the univariable analyses. The percentages of missing data were low, namely from between 0.0 to 2.5% for baseline characteristics, from between 0.0 to 0.8% for interventions, from between 0.0 to 0.1% for neonatal outcomes, and from between 1.4 to 2.7% for maternal outcomes. Therefore, cases with missing data were excluded. Multivariable logistic regression analyses were conducted for all births and stratified by the care setting, with adjustments for: parity; maternal age; ethnic background; socioeconomic position; and the degree of urbanisation. The results of the multivariable analyses were illustrated in figures with maps and boxplots with adjusted odds ratios (ORs) and 99% confidence intervals (CIs). The weighted overall intervention rate was taken as the reference. This weighted rate was the overall intervention rate, with the intervention rate of the region weighted for the number of women in each region. A confidence interval of 99% was chosen to limits chance findings due to multiple testing in a large dataset. Outcome variables were dichotomised and dummy variables were created to account for potential confounders in the multivariable logistic regression analyses. An important topic of this study, was to explore whether the variation of one intervention was associated with the variation of another intervention. Instead of exploring associations with eyeballing only, we quantified these associations by calculating Spearman’s rank correlation coefficients. These were calculated to demonstrate the associations of regional adjusted ORs between interventions in different care settings, and between interventions and childbirth outcomes. Correlation coefficients were calculated for the adjusted ORs of the regions, but only for outcomes that varied significantly between the regions. Since the sample size for all calculated correlations was the same, namely 12 regions, all correlations with ρ ≥ 0.57 or ≤ - 0.57 corresponded with a p-value of 0.05. Although the limits

27 Regional variations in childbirth interventions in the Netherlands for clinically significant correlations are arbitrary, we considered a correlation of ρ ≥ 0.60 or ≤ - 0.60 as strong (50), and only these correlations were discussed in the text and indicated in bold in the tables. Statistical analyses were performed using SPSS Statistics 22 (SPSS Inc, Chicago, IL, USA). First, overall results and remarkable associations between subgroups of women or between interventions were described. Second, results for each intervention were described, starting with those that showed most variation. Results Baseline characteristics Figure 1 shows the number of births eligible for inclusion in this study and table 1 describes the maternal and neonatal characteristics. Of the 276,701 births in nulliparous women, 153,091 were in midwife-led care at the onset of labour, 121,612 in obstetrician-led care, and for the remainder, the care setting was unknown. For births in multiparous women, these numbers were 174,918 and 161,286 respectively. In the regions, the proportion of mothers younger than 20 years of age ranged from between 0.8% to 2.2%, and of 40 years or older from between 2.4% to 4.5%. The lowest proportion of mothers with a non-Dutch ethnicity was 9.3% and the highest 34.6%. In three regions, there were no urban areas, whereas in all regions there were mothers living in rural areas, with a range of between 11.1% and 48.5%. Proportions of mothers with a low socioeconomic status varied from between 24.1% to 59.2%. Regions with the lowest number of births after 42 weeks (varying from between 0.8% to 2.5%), had higher numbers of births at 37-38 weeks (varying from between 5.8% to 9.2%), and vice versa. We found a similar pattern for birth weight below the 2.3rd, 10th or above the 90th or 97.7th percentile, with rates varying from between 1.4% to 2.1% for birth weight below the 2.3rd percentile, and from between 2.3% to 3.5% for birth weight above the 97.7th. 2

28 Chapter 2 Figure 1. Study population Results on the national level The greatest variation was found for the type of pain medication and whether a paediatrician was involved within 24 hours after birth, followed by variation in augmentation after a spontaneous onset of labour. Less variation was found for induction of labour and prelabour CSs, and least for instrumental vaginal births and intrapartum CSs (figures 2-7). Similar variation in intervention rates was found for births in midwife-led care compared to those in obstetrician-led care at the onset of labour in the same region (table 5). The adverse neonatal and maternal outcomes were not lower in regions with higher intervention rates (table 8).

29 Regional variations in childbirth interventions in the Netherlands Table 1. Maternal and neonatal characteristics of women by region GR FR DR OV FL GD UT NH ZH ZL NB LB Total n 19,441 22,568 15,875 42,869 17,461 71,286 52,893 105,948 139,573 11,327 84,187 31,302 Parity, % Nulliparous Multiparous 45.8 54.2 42.4 57.6 42.7 57.3 42.1 57.9 41.4 58.6 43.4 56.6 44.7 55.3 46.8 53.2 45.7 54.3 42.8 57.2 45.8 54.2 47.2 52.8 Maternal age, % <20 years 20-24 years 25-29 years 30-34 years 35-39 years ≥40 years 1.9 12.4 31.2 35.4 16.1 2.9 1.5 11.5 34.8 35.1 14.5 2.7 1.6 11.6 35.4 34.2 14.7 2.5 1.2 10.0 33.4 38.0 14.9 2.4 2.2 13.9 33.9 32.3 14.6 3.0 1.2 10.0 31.6 37.5 16.6 3.1 0.8 7.5 26.7 40.6 20.9 3.6 1.0 8.4 26.2 38.2 21.6 4.5 1.5 11.7 30.3 35.6 17.4 3.5 1.6 15.2 33.9 33.1 13.6 2.7 1.0 8.7 31.5 39.8 16.5 2.5 1.6 10.6 32.2 37.8 15.2 2.7 Ethnic background, % Dutch Non-Dutch 85.7 14.3 90.7 9.3 89.9 10.1 86.2 13.8 65.4 34.6 85.6 14.4 77.5 22.5 67.1 32.9 65.4 34.6 87.2 12.8 80.1 19.9 82.7 17.3 Urbanisation, % Urban Intermediate Rural 18.0 49.2 32.9 4.6 47.0 48.5 0.0 53.6 46.4 2.9 71.3 25.9 0.0 72.5 27.5 3.8 71.4 24.8 23.0 59.6 17.4 39.8 49.1 11.1 41.7 45.0 13.3 0.0 53.3 46.7 9.0 70.0 21.0 2.5 69.7 27.8 Socioeconomic status, % High (p ≥75) Medium (p 25-75) Low (p ≤25) 9.3 31.5 59.2 11.8 34.8 53.4 19.4 40.5 40.0 16.9 51.4 31.7 39.2 36.8 24.1 19.1 56.0 24.9 35.7 38.8 25.4 23.5 39.0 37.5 25.5 39.1 35.4 7.4 60.3 32.2 20.0 55.8 24.1 8.7 58.4 32.9 Gestational age (weeks), % 37+0 - 37+6 38+0 - 40+6 41+0 - 41+6 ≥42 8.7 71.5 17.9 1.8 8.3 71.7 18.1 1.9 9.2 72.5 16.9 1.4 8.6 72.1 17.4 1.8 8.4 73.1 16.9 1.6 6.7 71.6 19.3 2.3 5.8 71.2 20.7 2.3 6.6 72.2 19.0 2.2 7.6 72.8 18.3 1.4 6.5 71.6 19.4 2.5 7.3 72.4 18.5 1.8 8.8 73.9 16.5 0.8 Birth weight, % <2,3rd percentile <10th percentile >90th percentile >97,7th percentile 1.7 8.0 11.3 3.0 1.4 6.8 12.9 3.5 1.4 7.3 11.9 3.4 1.5 7.4 11.0 2.9 1.8 9.5 9.8 2.4 1.6 7.8 11.2 3.0 1.7 7.9 10.6 2.7 1.8 8.4 10.3 2.7 1.9 8.9 9.7 2.5 2.0 8.8 10.0 2.4 2.0 9.3 9.0 2.3 2.1 9.7 9.0 2.4 Percentage of missing data: 0.0% for maternal age, 0.4% for ethnic background, 1.1% for urbanisation, 2.5% for socioeconomic status, 0.2% for birth weight. 2

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