Discrete women only

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We used a mixed logit model, with setting modelled as an alternative-specific constant, and conducted a scenario analysis to evaluate the impact of changes in attribute levels on uptake of birth settings. There was ificant heterogeneity in preferences for some attributes. If the preferences identified were translated into the real-world context up to a third of those who reported planning birth in an obstetric unit might choose a midwifery unit assuming universal access to all settings, and knowledge of the differences between settings.

If all birth settings were available to women and they were aware of the differences between them, it is likely that more low risk women who currently plan birth in OUs might choose a midwifery unit. This is an open access article distributed under the terms of the Creative Commons Attributionwhich permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data are within the manuscript and its Supporting Information files.

The views expressed are not necessarily those of the Department. The funder provided support in the form of salaries for authors ORA, BF, JH, but did not have any additional role in the study de, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: Miranda Scanlon is a volunteer with BirthChoiceUK, a voluntary non-commercial organisation, which has in the past provided information to help women chose where to have their baby. Birth Choice website. All other authors declare no conflicts of interest. However, not all women have access to all options. A study found that in only 4. Despite this apparent increase in the provision of, and support for, different birth settings, most women still give birth in obstetric units. There have been a of studies investigating birth choice, carried out, in the main, in countries that actively promote birth choice for women at low-risk of complications, including the Netherlands[ 1011 ], Denmark[ 12 ], Australia[ 13 ], New Zealand[ 14 ] and Canada[ 15 ].

Given the discrepancy between the services that are available to women and where women actually give birth, the aims of this study were: to better understand what is important to women when making decisions about where to give birth; and to identify those service attributes Discrete women only women prioritise over others. This study was part of a broader project to generate evidence to inform decisions about the commissioning and delivery of maternity services that support choice.

Other factors include the birth settings that are available to women in their local area, whether these different settings are presented to women as options, the extent to which women have sufficient information to enable them to make an informed decision, and whether complications arise during pregnancy or labour which lead to changes in planned or actual birth setting.

While these factors are important in determining where women give birth, they are all beyond the scope of this study. Decisions in health often extend beyond what is most effective, to include other considerations such as costs, convenience, availability, ease of use and potential risks. Discrete choice experiments DCEs Discrete women only a well-established method used to understand the value individuals place on health and healthcare. In a DCE participants are presented with a series of choice sets.

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In each set the participant is presented with usually two hypothetical alternatives, each with differing levels of the attributes being investigated, and is asked to indicate which of the two options they prefer. In this study, in order to inform future service provision, we were interested in the factors that are important to women when making decisions about place of birth. By investigating how women would make choices about birth setting given full availability of all options, incorporating service attributes that may not be currently available to all women, this allows us to establish what women value irrespective of what is actually on offer.

A DCE helps us quantify the value that women attach to each birth setting and predict demand for future services. The extent to which the of stated preference studies including DCEs translate into real-world settings i. We used a of sources of information to inform the development and refinement of birth setting attributes. An initial candidate set of attributes and levels was developed using two systematic reviews [ 1617 ], and primary research a series of nationwide focus groups conducted for this project.

Thus the final Discrete women only included seven attributes, presented in Table 1 with their associated levels. Information presented to women about birth settings is available in Appendix A in S1 File. The DCE presented women with choice sets of hypothetical birth settings that differed according to the levels ased to the seven attributes of interest example of DCE question available in Appendix C in S1 File. The final of potential choice sets was identified using a D-optimal de approach allowing estimation of main-effects to generate the final choice set using Ngene ChoiceMetrics version 1.

The final de resulted in 60 choice sets divided into four blocks, and Discrete women only a D-efficiency score of These were followed by the DCE questions, a question about whether women had already made a decision about where to give birth, and if so where, and a of demographic questions: age, region, ethnicity, education, and employment. Women were also asked to rate the three most and least important attributes when making decisions in the DCE. After completion of the consent and pregnancy questions, participants were randomised to one of the four blocks of 15 choice sets.

The order of the questions within each block was also randomised. The online DCE survey was optimised to function with desktops, laptops and tablets but not mobile phones. We invited pregnant women over the age of 18 in England to take part in the survey. The study was first advertised on social media Twitter on 17 th January After slow recruitment and poor completion rates we engaged an online panel company to help achieve the target sample size.

The company conducted a brief survey of their panel to explore access to pregnant women with interest in completing our survey. Participating women identified through the online panel received reward points that could be redeemed for vouchers or goods on completion of valid surveys. Sampling through the on-line panel began on 9 th March and the survey was closed on 23 rd March Participant demographics were summarised using descriptive statistics. According to utility maximisation, woman n selected alternative i if the alternative maximised her utility satisfaction among all alternatives in the choice set.

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ificant ASCs coefficients indicate that there were elements of the decision not captured by the list of attributes in the DCE. The presence of heterogeneity in our sample is represented by the estimated standard deviations associated to each model parameter. ificant standard deviations indicate the presence of random heterogeneity.

All parameters including the ASCs were assumed to be normally distributed and we employed 5, random draws. We used Stata's modified Newton-Raphson algorithm for the maximum likelihood estimation and used the cluster option at the participant level to recognise that everyone completed 15 choice sets. We used the of the random parameter mixed logit to conduct a post-estimation scenario analysis to investigate what the predicted uptake of birth settings would be if all were available to women, i.

The predicted probabilities that women would choose each setting was estimated assuming participants had access to all four settings and all else being equal in this case all other attributes set to the baseline category. The impact single level changes have on overall predicted uptake e. All analyses were conducted using Stata 14 StataCorp.

Stata Statistical Software: Release The online survey was available for 51 days between 17 th January and 9 th Marchand was accessed by women, from advertising through social media, and from the on-line panel. Participant demographics are presented in Table 2 along with national data available from the Office for National Statistics. Women were represented geographically in similar proportions to national data. Participants completed the survey on average in just over nine minutes mean seconds, s.

Most women reported that an OU was available to them in their local area Fewer participants were aware of either an AMU We asked participants to choose the Discrete women only most important and three least important attribute when choosing where to give birth, and full are available in Table A in S1 File. Each attribute was important for some women when making decisions, and less so for others, highlighting the complexity of the decision making process.

from the random parameter mixed logit model are presented in Table 3. The ificant coefficients also indicate that the ASCs for each setting captured elements of the decision not included in the list of attributes in the DCE. All but one of the attributes time to see doctor had a least one ificant coefficient, indicating that they were all important when selecting a particular birth setting scenario. For continuity of care, having the same midwife throughout pregnancy and birth reference category was preferred over meeting a midwife for the first time during labour, although the other two levels of this attribute were not ificantly different to the reference category.

The coefficients for distance from home were negative and ificant, showing that all were less attractive than the reference category 0—15 minutes indicating that in general, women preferred a birth setting closer to home. Women had a strong preference for their partner to stay overnight with them, either on a Discrete women only shared with others, or in a room not shared with others.

Women preferred birth settings where the chance of a straightforward birth without intervention was highest. Safety for baby was a ificant driver of preferences, with women consistently selecting scenarios where safety was average or slightly better than average. This indicated that for these attributes there was considerable random unexplained variation across women. Our analyses show that predicted probabilities of choosing each setting and assuming participants had access to all four settings and all else being equal : These figures are shown in Table 4 along with the choices actually reported by women in this study full of scenario analyses is available in Appendix E in S1 File.

We also investigated the impact of single level changes to attributes again all else being equal on predicted uptake, and these are shown in Table 4. Similarly, increasing safety for baby by the same level for an FMU was associated with an increase in predicted uptake of We investigated which attributes, or characteristics of care, are important to women when choosing where to give birth.

Women preferred midwifery units MUs and obstetric units OUs to planned home birth, and this mirrored the decisions made by women in this study who had already selected their setting. Participants were generally open to choosing all settings, with OU and midwifery units preferred to home birth.

However, only half Discrete women only the women in our study reported being aware of having access to an AMU, and a third were aware of the availability of an FMU. Asking participants to make a of choices between hypothetical scenarios enables us to build up a picture of what is important in decision making, including service attributes which may not currently be available to all.

We can only learn so much from asking women about their experiences of birth setting and choice when provision of services and information is variable. For example, even when local provision is poor women may report being satisfied with care because they may not be aware of better alternatives.

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A comprehensive list of attributes was included, however it is possible that, for some women, not all attributes of importance were included. DCEs present information in a way that attempts to model how decisions are made in real-life, involving many competing and overlapping factors.

Recruitment to the study was slow when advertising through social media, therefore a decision was made to engage an online panel to identify and invite potentially eligible women. While this approach helped to recruit more than the initial projected sample size, it does have limitations. Participants in the online panel were incentivised using reward points for completion of the survey, so it may be the case that some participants may have completed the survey without engaging with the questions simply in order to receive the award.

While the sample of women in this study matched that of the general population on a of demographics, the women who participated tended to be better educated, were likely to have had a greater interest in choice of birth setting than the wider population, and may have been more likely to consider planning birth in a MU or at home. It is worth reiterating that we present data on where pregnant women would like to give birth, along with their stated preferences for birth settings, however we do not know where they went on to give birth, or to what extent this reflected the choices they had already made.

Care should be taken in interpreting the of this study beyond the included sample. When conducting internet based surveys it is often difficult to calculate a response rate the proportion of participants who saw the advert and then took part in the study. This is an important factor as it can tell us something about the differences between those who took part and those that did not.

While the sample of women in this study matched that of the general population on a of demographics, the women who participated tended to be better educated, were likely to have had a greater interest in choice of birth setting than the wider population, and were more likely to consider planning birth in a MU or at home. Participation was also limited to being completed on a computer or tablet, therefore excluding those who only had access to a smart phone.

Also, a quarter of women reported that they had risk factors that might contraindicate birth outside an OU, which might have therefore limited their actual choices. It has been four years since national guidelines in England were updated to include, for the first time, topics to frame discussions about birth choice with pregnant Discrete women only.

This study investigated some of the same attributes investigated in studies, including continuity of care, distance Discrete women only the unit, and availability of medical staff. However our study also included several attributes that have not been included in other studies, such as reputation, safety for the baby, intervention rate, and the possibility for partner to stay overnight, all of which were shown to be important to women when choosing a birth setting.

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One explanation for why women at low risk of birth complications still overwhelmingly give birth in OUs is that there may be a misperception that it is safer, due to having health care professionals at hand. But we also found in our scenario analysis that the chance of having a straightforward birth without intervention was another important factor for women.

This is a notable finding. The Birthplace study provided good quality evidence about the relative safety for the baby of each birth setting, showing that for nulliparous and multiparous women, MUs were as safe as OUs, Discrete women only there is also strong evidence from Birthplace and other studies that planning birth in a MU is associated with ificantly increased chances of having a straightforward birth without intervention.

It could also be the case that women who choose birth in an OU are not aware of other birth options. It has been shown that women and professionals often assume that birth will take place in the hospital environment.

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So the challenge of impacting on these assumptions is not inificant. We have shown in this study that the option for their partner to stay overnight after the birth was important to women in their decision-making about their birth setting. Future work should investigate whether the random heterogeneity observed in this study le to specific patterns of preferences that can be mapped onto available birth settings.

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If all birth settings were available for women, and they were fully informed about the benefits of each of them, it is likely that more low risk women currently giving birth in OUs would choose to plan birth in a midwifery unit. Appendix A. Experimental de. Appendix B. Sample size calculation. Appendix C. Screenshot of DCE question as it appeared on-line. Appendix D. Information about birth setting.

Discrete women only

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