Introduction
The chapter commences with an overview of drivers for high-quality maternity care, followed by a discussion of evidence relevant to defining and measuring quality and safety in maternity care, use of routine data, maternity health-care workforce, quality and safety indicators and health-care workforce and efficiency, and other literature to inform study aims and objectives.
Several bibliographic databases, including PubMed, The Cochrane Library, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), EconLit and that of the National Institute for Health and Care Excellence (NICE), were searched for relevant primary and secondary studies and guidelines. Search terms included ‘maternity care’, ‘safety’, ‘quality’, ‘outcomes’, ‘midwifery care’, ‘obstetric care’, ‘acute hospital settings’, ‘maternity workforce’, ‘cost effectiveness’, ‘efficiency’, ‘production function’ and ‘stochastic frontier’. No date limits were used and only studies published in English were considered. Relevant policy reports were identified from searches of relevant websites of government and professional organisations.
As a systematic review was not conducted, a formal search strategy was not developed; however, priority was given to evidence relevant to UK-based maternity care, including studies undertaken in high-income countries with similar maternity systems (i.e. where midwives and obstetricians were responsible for providing care) which considered quality and safety of maternity care. Evidence relevant to health-care workforce issues that may or may not have been conducted in UK maternity settings was also considered.
High-quality maternity care
All NHS providers have a mandate to enhance the quality of patient care, and the performance of maternity services has been viewed as a window into whether or not quality health services in general are being delivered.1 The Department of Health’s National Service Framework for Children, Young People and Maternity Services,2 with its 10-year time frame for implementation, and Maternity Matters3 were consistent in the commitment to deliver a choice of safe, accessible, high-quality maternity care which was women focused and family centred. Underpinning principles included the view that pregnancy and birth are normal life events, maximising the opportunity for all women, regardless of risk profile, to have as physiological and positive a birth experience as possible. The coalition government has not developed or published a cohesive maternity policy but has published indications of its commitment to women’s choice of maternity care,4,5 continuity, and improved outcomes.6
The professional commitment to improving the quality of maternity care, and sustaining the workforce to achieve this, has been led by the relevant Royal Colleges, including the Royal College of Midwives (RCM) and Royal College of Obstetricians and Gynaecologists (RCOG), often with joint College publications. In 2007, a joint report was published by the RCOG, the RCM, the Royal College of Anaesthetists and the Royal College of Paediatrics and Child Health (RCPCH) to provide guidance to develop equitable, high-quality standards for UK maternity care.7 Standard 30 (out of 30) focused on provision of a high-quality workforce and promotion of appropriate leadership, skill mix and competencies in midwifery, obstetrics, anaesthetics and paediatrics. A 2009 position statement from the RCM on staffing standards in midwifery services to assist commissioners and providers, endorsed by the RCOG and RCPCH, recommended a minimum ratio of 1 midwife per 28 births per year. Falling below this ratio would be a strong indication that a service should undertake a thorough workforce review.8 The RCOG report High Quality Women’s Health Care9 emphasised how high-quality care could promote health over the life course for women and their infants. It proposed changes that focused ‘on the needs of the woman and her baby by providing the right care, at the right time, in the right place, provided by the right person and which enhances the woman’s experience’ (Foreword, page iv), and the fundamental role that midwives have in delivering high-quality health care also continues to be recognised.10
Defining quality
One of the key issues to address in any health system is how to derive robust, appropriate and usable measures of the quality and safety of care and measures of outcomes of care which resulted in harm. The ideal measure should be easy to define and observe, should reflect priority outcomes for patients, clinicians and service providers and should identify those areas where quality of care/outcome could be improved.
The Institute of Medicine11 identified six dimensions of quality: namely that health care should be safe, effective, patient-centred, timely, efficient and equitable. Some of these have been widely adopted within the UK.12–14 The current UK government has taken the approach that process indicators or targets are unnecessarily bureaucratic and distract from the important objectives of improving safety, reducing morbidity and improving patient experience more broadly, and has therefore focused quality measurement on evidence-based clinical outcomes.15 The resulting NHS Outcomes Framework provides a national overview of the quality of NHS care, provides accountability and acts as a catalyst for driving quality improvement involving five domains:16
preventing people from dying prematurely
enhancing quality of life for people with long-term conditions
helping people to recover from episodes of ill health or following injury
ensuring that people have a positive experience of care
treating and caring for people in a safe environment and protecting them from avoidable harm.
The NHS Outcomes Framework for 2013/14 introduced new outcome measurements.6 Improvement areas in maternity care and their relevant indicators include:
reducing deaths in mothers (or at least maintaining the low level) (domain 1)
reducing deaths in babies – neonatal deaths and stillbirths (domain 1)
helping women to recover from ill health or injury following birth (domain 3)
improving women and their families’ experience of maternity services – women’s experience of maternity services (domain 4)
treating and caring for people in a safe environment and protecting them from avoidable harm (domain 5).
The maternity indicators identified by the Outcome Framework, whilst important in determining quality of care, cover only a small aspect of maternity services, and other bodies have also outlined key indicators. For example, the UK-wide Midwifery 2020 recommended in its report Delivering Expectations13 a number of additional indicators to measure the quality of midwifery care. These included reducing perineal trauma; uninterrupted skin-to-skin contact between mother and baby following the birth; continuity of midwife care; and increasing the normal birth rate, using the definition of normal birth published by the Maternity Care Working Party in its consensus statement.17 The Commissioning for Quality and Innovation (CQUIN) framework was introduced in 2009 to enable commissioners of health services to reward excellence by giving financial incentives to local health-care providers to deliver nationally agreed quality improvements and better outcomes for patients. There are four National CQUIN Scheme goals in 2013/14.18 These are the Family and Friends Test (introduced for maternity in October 2013), dementia care, venous thromboembolism and the NHS Safety Thermometer,19 of which there is a maternity version currently in development by the NHS Quality, Efficiency and Support Team (NHS QUEST). Local CQUIN schemes agreed with local commissioners should be in place in late 2013, and collaboration is encouraged where contracts are made with several commissioners. Commissioners are encouraged to use appropriate existing indicators such as the Indicators for Quality Improvement (IQI),20 Advancing Quality Alliance,21 the NHS Atlas of Variation and NICE Quality Standards.22 Maternity specific indicators used for CQUIN include IQI indicators for smoking cessation during pregnancy, prevalence of breastfeeding at 6–8 weeks and access to maternity services by 12 weeks + 6 days.20 Indicators of quality, therefore, should measure a balance of aspects of harm or adverse outcomes, care which promotes health and patient-derived measures of the experience of care.
Measuring quality
Measuring quality of health care is not a new concept, and Donabedian described his approach in a paper published in the Journal of the American Medical Association.23 It was based on three components: structure, process and outcome, which were viewed as inter-related. Structure referred to the conditions under which care was provided, including staffing levels and mix, facilities and equipment, and organisational characteristics such as supervision and performance review. Process referred to activities carried out and care provided, such as diagnosis, treatment and education. These activities are often carried out by staff. Outcomes referred to changes – good or bad – in individuals which could be attributed to the health care received. Donabedian differentiated these measures from actual aspects of quality, instead considering them to be alternative types of information which could be used to infer or indicate good quality. He highlighted the necessity of using these only when there was a relationship of cause and effect between the three components.
The King’s Fund report Getting the Measure of Quality24 still considered this approach to be useful in developing quality indicators, although the RCOG has pointed out that some obstetric indicators are hard to classify in this way25 – for example, a caesarean may be a process initiated by a clinician or an outcome following another intervention and may have a good or bad effect (or both) on the health of two individuals, mother and baby.
Structural characteristics – the way a health-care system is organised – may have an important impact on the quality of care provided. The Francis Report26 has highlighted how failings in management and governance caused serious failings in the processes and outcomes of care, but the direct relationship between these can be difficult to assess.
Where it is known that particular processes, such as continuity of care, are signifiers of quality of care, for example because they impact on some aspect of quality such as safety or patient experience, the measurement of the extent of that process can be used as an indicator of quality.23 Process measures have the advantage of directly measuring care that is received by patients and potentially increasing the detection of poor care. They are capable of being measured contemporaneously, giving a more immediate assessment of quality. However processes can also be hard to measure and data may not be available. They can be subject to manipulation or ‘gaming’, particularly when performance is being assessed externally or financial incentives are at stake.
Clinical outcome measures have advantages over process measures in that they can assess the health outcomes (favourable or unfavourable) of patients who have received care. Outcomes are often routinely collected, making the data more readily available for analysis. They can be less subject to manipulation, but have the disadvantage of being affected by factors other than care, such as coding accuracy, disease severity, comorbidities and other independent characteristics or demographics. As it is not always possible to directly attribute outcomes to specific processes of care, in reality it may be more difficult than assumed to use outcome indicators to improve the quality of care in this way.
Measuring quality, safety and harm in maternity care
Although maternity care is the commonest reason for hospital admission among women aged 15–59 years in the UK,27 there is lack of agreement on what measures should be used. Safety measures developed for general populations often do not include measures appropriate for pregnant or postnatal women.28 There are also methodological issues to address, for example population, context of care, data quality, variation in outcomes within and between units, and whether or not risk adjustment was used to address confounding factors.
It is increasingly recognised that measures traditionally used in maternity care such as maternal and neonatal mortality are essential, but also that other measures of quality of care are needed.29 Studies to date which have reported the development of a quality measure for maternity care have tended to focus on patient-specific indicators (PSIs), such as primary caesarean delivery rate,30 or obstetric trauma in caesarean, instrumental and unassisted deliveries [used by the Agency for Healthcare Research and Quality (AHRQ)31] or aspects of patient satisfaction.32
Several studies have considered the risk of interventions in maternity care (e.g. caesarean birth), which could inform their use as a potential measure of quality. Paranjothy et al.33 from the NICE Collaborating Centre for Women and Children examined the variation in caesarean rates between maternity units looking at case-mix differences in a national prospective cross-sectional study. Data were collated from 216 maternity units in England and Wales on women who gave birth between May and July 2000. The relationship between case-mix characteristics and odds of a caesarean birth before or during labour was investigated using logistic regression models. Overall caesarean rates standardised for case mix were then calculated for each maternity unit. Heterogeneity between units was examined using random-effects meta-analysis. Adjustment for case-mix differences explained 34% of the variance in caesarean rates. The odds of having a caesarean birth before and during labour increased with maternal age. Women from ethic minority groups had lower odds of caesarean birth before labour and increased odds in labour. Women who had a previous vaginal birth had lower odds of caesarean, although the magnitude of this for caesarean before and during labour was markedly different. Findings showed that the variation in organisation of services, women’s preferences for mode of birth, staffing levels and clinician attitudes were all important factors to consider when quality and appropriateness of maternity care are evaluated.
Using routine data
Hospital Episode Statistics (HES) provides information on care provided by NHS hospitals and for NHS hospital patients treated elsewhere in England. It is the data source for a wide range of health-care analysis for the NHS, government and many other organisations such as the private benchmarking services CASPE Healthcare Knowledge Systems and Dr Foster.
Every episode of hospital inpatient care generates a patient record which includes demographic information along with details of the episode of care, diagnoses using International Classification of Diseases version 10 (ICD-10) codes34 and procedures such as operations using Office of Population Censuses and Surveys Classification of Interventions and Procedures (OPCS-4) codes.35 Where a woman’s record additionally includes the delivery of a baby, limited further information on the birth is collected in a ‘maternity tail’ to the mother’s record. Individual HES records for the same person can be linked across time and providers to enable long-term patterns of care to be studied. The individual records of mother and baby are not linked.
Some researchers have compared outcomes of the US AHRQ PSIs, including obstetric trauma indicators, with routinely collected HES inpatient data, in an attempt to generate quality indicators for England. Raleigh et al.36 compared UK and US data for nine PSIs using a case–control analysis of HES data for 2003–4, 2004–5 and 2005–6 for all English trusts. Length of stay and mortality between cases (patients experiencing the particular safety event measured by an indicator) and controls were matched for age, sex, health resource group (standard groupings of clinically similar treatments that use similar levels of health-care resource), main specialty and trust. They found some consistency in national rates for the nine indicators and, for all but one indicator, hospital stay and mortality were longer. The authors concluded that internationally comparative indicators could be derived from English data, although further validation was needed, and recording needed to be improved.
Bottle and Aylin37 also compared outcomes for nine AHRQ indicators (10 were originally selected but one – iatrogenic pneumothorax – was dropped for lack of equivalent ICD-10 code) for use in English routine data in relation to established measures of negative outcome including mortality. Using case-mix adjustment they found wide variations between trusts which were potentially due to inadequate adjustment, differences in coding definitions between trusts, or poor quality of coding. They concluded that the derivation of patient safety indicators from HES data were potentially useful for prospective evaluation of data quality.
Studies using HES data show that despite data quality issues, analyses using judicious cleaning and case-mix adjustment can be useful in identifying variations in patterns of maternity care. Some of the results showing the importance of case-mix adjustment using HES data have been validated using data from the Millennium Cohort Study.38 This study showed that, for women having their first baby, operative birth rose with increasing maternal age, and that for all women mode of birth differed significantly by ethnicity.
Obstetric outcomes derived from HES inpatient maternity data from 146 English NHS trusts were analysed by Bragg et al.39 in a cross-sectional analysis to ascertain if variation in unadjusted rates of caesarean births could be explained by maternal characteristics and clinical risk factors. The main outcome measure was rate of caesarean birth per 100 births (live or stillborn). The population included women aged 15–44 years who had a singleton birth between 1 January and 31 December 2008. The likelihood of women having a caesarean birth given their age, ethnicity, parity, socioeconomic status, deprivation status and clinical risk factors (including previous caesarean birth, breech presentation and fetal distress) were entered into a multiple logistic regression model.
A total of 147,726 (23.8%) women had a caesarean birth, which was more likely if they had a previous caesarean or a breech presentation. Elective and emergency caesarean rates by NHS trust were adjusted for maternal characteristics and clinical risk factors, using funnel plots to show significant variation between trusts. Maternal age, ethnicity and parity (particularly in multiparous women with a previous caesarean) were all significant factors in determining the likelihood of a caesarean birth. A number of clinical risk factors, including diabetes, hypertension and placental problems, also predisposed women to having a caesarean. However, adjusting for maternal characteristics and clinical risk factors did not greatly reduce the variation between individual trusts, with the observed variation in caesarean rates being 14.9–32.1%. This variation was largely due to emergency caesarean rates, rather than elective caesarean rates, which showed much less variation. The authors concluded that case-mix adjustment was necessary in order to compare caesarean rates between trusts, and that the remaining variation may be due to differences in clinical practice regarding emergency caesarean between trusts.
This work was furthered by the RCOG in its Clinical Indicators Project,25 which identified 11 potential performance indicators derived from HES data, the basis for selection being validity (reflecting quality of care), fairness, sufficient statistical power and ability to technically code the outcome adequately. In addition, the suite of indicators had to cover various dimensions of care to give a balanced picture of the service. The RCOG identified key issues with HES maternity data quality, particularly duplicate records, records not relating to deliveries and incomplete or inconsistent recording of data items. Despite extensive data cleaning to remove duplicates and records which did not relate to a delivery episode, identifying units with inconsistent or missing data and adjusting for case mix, this limited the use of some potential indicators. Results were stratified between nulliparous and multiparous women and adjusted for maternal age, ethnicity, social deprivation and a number of clinical risk factors such as previous caesarean, diabetes, hypertension, gestational age and birthweight. The results were shown in funnel plots and demonstrated a large variation in intrapartum processes and outcomes which could not be explained by random fluctuations. For example, among women giving birth for the first time, there was a twofold difference between hospitals with the highest and lowest rates of emergency caesarean after induction of labour (20% compared with 40%) and of instrumental birth (16% compared with 32%). The report concluded that further understanding of the unexplained variation is important in order to compare performance across trusts.
Organisational factors, such as trust configuration, size, models of care, staffing levels, skill mix, staff deployment and safety culture, remain unknown factors in the understanding of important influences of the quality and safety of maternity care, some of which are considered in the next section.
The maternity health-care workforce and quality and safety indicators
‘The performance of maternity services is a touchstone of whether we are delivering quality based on patient safety, effectiveness of care and patient experience.’40 This statement from the NHS Chief Executive, David Nicholson, followed the review of maternity services carried out by the Healthcare Commission [now the Care Quality Commission (CQC)] in 2007. This review raised key concerns that, in some trusts, levels of staffing were well below average and may have been inadequate. It recognised that staffing was a contentious issue, as it underpins the quality of the service but at the same time is the most costly element of providing that service.41
In 2008 the report of the independent inquiry commissioned by The King’s Fund, Safe Births: Everybody’s Business,42 and the Healthcare Commission’s review of maternity services, Towards Better Births,41 identified areas in need of improvement, including staffing, training and communication. Staffing has been identified in numerous reports as being a critical component of safe, effective, patient-centred care. Staffing levels contributed to 3.5% of all reported safety incidents across the NHS43 with workforce factors likely to contribute to a far higher total proportion. This has resulted in the dilemma of maintaining, and ideally improving, the quality and safety of care in a maternity service facing greater demand and increasing complexity in the health of childbearing women.
Research from a number of sources, including studies of other health-care professionals, points towards better-quality care, improved outcomes or fewer adverse events being associated with higher levels of registered nurse staffing.44–49 However, while reduced complication levels may be associated with reduced length of inpatient stay (improved productivity), the association between higher registered nurse staffing and reduction in stay is not universally supported. Kane’s systematic review found evidence to support such a relationship in intensive care and outcomes for surgical patients but not for medical patients, and further highlights complexity and the challenges of attributing cause.50
There is also a body of literature on the optimal level of staffing for doctors from the USA51,52 and some evidence of a medical staffing outcome relationship from both the USA and the UK.44,53 Other evidence that considers medical staffing suggests complex inter-relationships between workload, efficiency and quality.54–58 However, this literature is more limited in extent than that on nurses, and there are also significant concerns with drawing causal inference from the extant literature to the UK maternity workforce.
The complementary and substitutability of nurses/midwives and doctors is even less well documented in large studies exploring routine practice (as opposed to experimental implementations). Outcomes may be sensitive to ratios between nurses and medical staff. For example, in the UK, a higher total of clinically qualified staffing (doctors + nurses) per bed and a higher number of doctors relative to the number of nurses were both associated with lower mortality-based failure to rescue in the fully adjusted analysis.57
In maternity care, a survey of health-care professionals showed that many believed that low staffing levels have a direct impact on safety of maternity services as a result of increased error rates, burnout, tiredness and less direct care.58 Respondents were of the opinion that higher midwifery staffing levels would allow all women to have one-to-one care in labour, reduce intervention rates, reduce postnatal hospital stays and release money to reinvest in services.
However, few studies have investigated the link between obstetric and midwifery staffing and outcomes.42 Joyce et al.59 drew on cross-sectional data from all 65 maternity units in the Thames region between 1994 and 1996, covering a total of 540,834 live births and stillbirths. After adjustment for birthweight, perinatal units with a more ‘interventionist’ approach (defined by higher rates of caesareans, epidurals and instrumental births) and higher levels of consultant obstetric staff were found to be associated with lower stillbirth rates; and this effect persisted after adjustment for other possible predictive and confounding factors. An analysis using HES 2008 data matched with staffing variables from the Maternity Matters Benchmarking Dataset found a relationship between higher levels of full-time equivalent (FTE) midwifery staffing and a lower chance of readmission at 28 days; however, risk adjustment was limited.60 However, observational studies have limited capacity to identify causal pathways.
The NHS Operating Framework 2010/11 identified the need to help local managers to identify optimum skill mix for quality and productivity.1 Birthrate Plus (BR+) is widely used in the UK to calculate the number of midwives required in a NHS maternity unit.61 Despite the widespread use and recommended use of BR+, it is not known whether ratios or staffing establishment numbers reflect ‘the ideal’ or ‘what is current’ and how these are related to providing a high-quality and safe maternity service.
Strategic approaches to maternity support worker development are under way at a national level in Scotland, Wales and Northern Ireland. However, there is limited and inconclusive evidence that changing workforce skill mix or substitution of roles in maternity care and other acute or primary care settings is associated with improved health outcomes or a reduction in costs. Few, if any, studies have considered the potential trade-offs between staff groups to optimise quality and efficiency, nor have they attempted to explore differential effects on different outcomes simultaneously.
Health-care workforce and efficiency
The majority of the literature on the relationships between the health-care workforce and outcomes including efficiency and effectiveness is based within acute secondary care. Very little relates specifically to maternity services, although there may be lessons to learn. Work examined so far points to a relatively simple gradient of improving outcomes with more registered nurses, and improvements in both outcomes and cost-effectiveness with richer skill mix.
Moving beyond the nursing workforce, economic evaluations of nurse for doctor substitution (which could be construed, in part, as involving a dilution of skill mix) also suggest that such substitution can be cost-effective or lead to a net cost reduction.62 The optimal use of scarce and expensive labour resources will depend upon whether or not they are complements or substitutes. There are two common approaches to this question used in the production economics literature: p-complementarity and q-complementarity.63,64 Traditionally, p-complementarity is evaluated from a cost function, but in health-care applications cost data are not often available for all inputs. However, q-complementarity can be investigated via the production function, but is not often addressed. A rare example of this approach to health care is by Thurston and Libby,65 who estimated the staffing relationships for primary physician services in the USA. They found that nurses are q-complements for physicians, while technicians and unqualified nurse aides are q-substitutes for nurses, in the production of primary care visits. Economically, very little is known about the complementarity or substitutability of staff groups (skill mix) within the NHS, despite there being critical changes in the composition of the workforce over recent years. We have not found examples which address this important question from within any acute care settings.
Some of the economic models above also point to two conceptually distinct ‘outcomes’ for a given health-care team: quality (represented primarily by patient safety in the existing literature) and productivity (represented by volume of cases treated or length of stay). This is also embodied in the current NHS Quality, Innovation, Productivity and Prevention (QIPP) programme, which seeks to improve quality and productivity simultaneously, although it is not clear whether improvements in both are separate, linked or traded off. While there is considerable evidence on the benefits of investment in improved patient safety, very little is known about the impact on a health-care provider’s efficiency and output of diverting resources to this cause.66
Cost-effectiveness and effective use of fixed resources involving alteration in the composition of the clinical team is clearly dependent upon wage differentials. Replication and extension of US findings in other health economies is clearly warranted. It also seems clear from the existing evidence that there is unlikely to be a general relationship between skill mix and quality/productivity that generalises across care settings. Furthermore, all the above-cited economic models are limited because the staffing variation observed in cross-sectional observational studies is assumed to be causing the differences that are observed. The effect of variation associated with nurse staffing is assumed to be accurately determined by parameters derived from regression equations, even though it is clear that neither costs nor outcomes are the result of a deterministic process.
In relation to economic evaluations of skill mix change and outcomes, this research in general is limited.62 Jones et al.57 noted that, while there are a few hospitals that have relatively low staffing levels but appear to produce good outcomes, there are hospitals with high staffing levels that appear to produce poor outcomes. This suggests that high staffing levels may be merely indicative of aspects of care, and existing economic models have simply presumed that the relationships observed are causal. However, it is unknown if reductions in staffing levels and mix would produce a corresponding reduction in outcome. Improving outcomes through staffing changes is not costless for health-care providers and standard microeconomic theory would suggest that they are subject to diminishing marginal returns. This notwithstanding, little is really known about the impact of variations of workforce and skills mix either positively or negatively in relation to health-care providers’ operational efficiency or the potential to substitute one grade of staff for another (e.g. nurses for doctors, health-care support workers for nurses, clinicians for managers, let alone midwives for obstetricians) and its impact upon outputs.
Some studies have considered the costs of providing maternity care and how costs vary between hospitals. Laudicella et al.67 undertook an analysis using patient level data comparing obstetric departments between hospitals. They examined the effect of patient characteristics on costs and considered factors to explain differences in costs between hospitals. Using HES record data, they mapped costs to individual patients and found that costs were driven by women’s characteristics to a greater extent than was explained by the type of birth they had. Costs were higher for women who lived in an area of greater social deprivation or had a number of obstetric risk factors. Even after adjusting for maternal characteristics and the type of birth as identified by the Health Reference Group (HRG) code, they found large variations in costs of obstetric care. They proposed that these might arise from differences in coding practice, differences in how costs were apportioned within accounting systems or differences in efficiency.
Further work done at the Centre for Health Economics, York,68 considered cost and length of stay for women having babies. They found that older women, those with more risk factors, those from poorer areas and those having more complex births with interventions had a longer stay and higher costs. These factors have been rising for several years and continue to increase.
Task-shifting offers another possible route to cost savings. There is no robust evidence about the cost-effectiveness of maternity support workers. Several studies have sought to compare the costs of midwife-led care with consultant/medically led care. The studies use a variety of methods in their costing calculations and some include elements of ante- and postnatal care in addition to the intrapartum period. This makes it difficult to draw conclusions.
A comparative analysis of normal hospital birth in nine European countries confirmed the importance of labour costs and skill mix as determinants of total delivery costs.69 While medical tests and drugs accounted for only 1–10% of these costs for all countries, staffing accounted for as much as 74% of total costs in Germany and 63% in Spain, although the equivalent figures were only 25% in Italy, 28% in Denmark, 34% in France and 42% in England. Denmark, France and England are identified as examples of countries that primarily use midwives to provide support before, during and after birth, while Germany and Spain almost always have an obstetrician present during birth, which accounts for their additional staff costs. The researchers conclude that higher nurse-to-physician ratios reduce costs because midwives and nurses are able to take on many medical tasks that would otherwise be performed by doctors.
Five studies in a Cochrane review that compared continuity of midwife-led with shared or medical-led care in 13 trials involving 16,242 women at low and mixed risk included cost data, using different economic evaluation methods. All found savings associated with midwife-led intrapartum care. Although the studies were inconsistent in their approach to estimating maternity care costs, it seems there is potential for cost-saving with midwife-led care.70 Based on scant existing evidence, there appears to be a trend towards a cost-saving effect for midwife-led continuity care compared with other care models.71 The estimated mean cost saving for each eligible maternity episode is £12.38. This translates to an aggregate saving of £1.16M per year, if half of all eligible women avail themselves of midwife-led care at booking. This equates to an aggregate gain of 37.5 quality-adjusted life-years (QALYs) when expressed in terms of health gain using a NICE cost-effectiveness threshold of £30,000 per QALY. The uptake of midwife-led maternity services affects results on two levels: first by its role in determining caseload per midwife and thus mean cost per maternity episode; second at the aggregate level by determining the total number of women who start in midwife-led services nationally.72
Other cost drivers
Staffing is not the only driver of costs in maternity services. Other factors, such as equipment use, also play a part. In addition, factors such as the mode and place of birth have implications not just for costs but also for staffing requirements. The delivery setting has clear implications for staffing levels and skill mix. The cost-effectiveness of alternative planned places of birth was assessed with individual-level data from the Birthplace national prospective cohort study in 147 trusts in England between 2008 and 2010 involving 64,538 women at low risk of complications before the onset of labour. Incremental cost per adverse perinatal outcome avoided, adverse maternal morbidity avoided and additional normal birth were costed. The total unadjusted mean costs were £1066, £1435, £1461 and £1631 for births planned at home, in free-standing midwifery units (FMUs), in alongside midwifery units (AMUs) and in obstetric units (OUs) respectively. Much of the cost saving was attributed to lower caesarean rates in non-OU settings. For multiparous women at low risk of complications, planned birth at home was the most cost-effective option. For nulliparous low-risk women, planned birth at home is still likely to be the most cost-effective option but is associated with an increase in adverse perinatal outcomes.73
The staffing costs of intrapartum care delivery are difficult to identify because of the complexity of disentangling not just the intrapartum element from ante- and postnatal care, but also the staffing component from associated costs, such as birth setting, mode of delivery and length of stay. A further difficulty in interpreting the evidence is that the available data come from different national systems of maternity care, which makes direct cost comparison difficult. Given this, the evidence of the financial implications of different staffing models is limited. The available evidence, however, suggests that midwife-led models of care and out-of-hospital midwife-led settings could provide a safe and, in many cases, cost-effective alternative to medically led intrapartum care.
The evidence presented in this chapter has highlighted limited empirical evidence regarding the impact of maternity workforce staffing and skill mix on the safety, quality and cost of maternity care in the UK, variations in outcomes and women’s experiences of care, despite a number of policy drivers and recommendations. The aim of this project was to understand the relationships between maternity workforce staffing, skill mix, cost and a range of outcomes including patient safety and quality indicators, and efficiency. The research aimed to answer the following questions:
How do organisational factors affect variability in maternal interventions and maternal and perinatal outcomes?
What is the relationship between maternity staffing, skill mix and maternal and perinatal outcomes?
What is the relationship between maternity staffing, cost and outcomes?
