The aim of this study was to understand the relationships between maternity workforce staffing, skill mix, organisational factors and a range of outcomes including patient safety and quality indicators and efficiency. Therefore this research aims 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?
We conducted secondary analysis of data from HES from 143 NHS trusts in England for the NHS year 2010–11 and NHS Workforce Statistics, England: 2010–11. We included mother’s characteristics, measured at the individual level, that are known to affect the outcomes of interest. These included age, parity, clinical risk at the end of pregnancy as measured by the NICE guideline for intrapartum care, ethnicity, area socioeconomic deprivation as measured by the IMD, geographical location (urban/rural) and region.
Characteristics measured at trust level included size measured by number of deliveries, teaching status, maternity configuration (whether AMU and FMU are part of trust provision or not), and staffing variables. These included staffing levels (FTE obstetric medical staff, midwives and maternity support staff/100 maternities, FTE all staff/100 maternities) and skill mix (doctor/midwife and midwife/support worker ratio).
We looked at 10 process and outcome indicators derived from HES data informed by needing to have a balance of positive and negative indicators, the importance to women, costs, and the availability and quality of coding within the HES data set. Three indicators were derived to indicate a healthy mother and healthy baby, thus reflecting a concept of harm-free care, avoidance of longer-term morbidity and a positive outcome. The mode of birth indicators were chosen to compare important processes and outcomes across trusts and with other studies. These were:
mode of birth: comprising (1) delivery with bodily integrity (without caesarean, uterine damage, second-/third-/fourth-degree tear, sutures and episiotomy), (2) normal birth (without induction, instrumental and caesarean birth, episiotomy, general and/or regional anaesthetic), (3) spontaneous vaginal delivery and (4) intact perineum
caesarean indicators: (5) elective caesarean, (6) emergency caesarean, (7) all caesareans
healthy mother indicator: (8) delivery with bodily integrity, mother returned home within 2 days, not readmitted within 28 days and without instrumental delivery, maternal sepsis or anaesthetic complication
healthy baby indicator: (9) baby’s weight 2.5–4.5 kg, gestational age 37–42 weeks, live baby
healthy mother/healthy baby dyad: (10) healthy mother and healthy baby.
Two-thirds of women were aged between 20 and 34 years (67%); most either were nulliparous (43%) or had one previous live birth (32%). Women were more likely to be classified as higher risk than lower risk (55% vs. 45%) according to the definition of women at increased risk of complications based on the NICE intrapartum care guidelines.80 Included in the 55%, were 4% of women who required individual assessment to determine if they were at increased risk of complications. About two-thirds of women were categorised as white British, a further 9% were from another white background, 4% were Pakistani, 3% were African, 3% were Indian and 2% were from other Asian backgrounds. A higher proportion of women were living in a deprived area based on the IMD: 28% compared with 15% from the least deprived quintile. Most women lived in denser urban areas (86%).
Multilevel logistic regression models, in which mothers were nested within trust, were fitted to the 10 maternity indicators. When considering the effect of maternal characteristics, women’s outcomes were largely determined by their clinical risk (based on NICE guidance80) and parity, with higher-risk women and nulliparous women generally being more likely to have interventions and less favourable outcomes. Age was the next most important factor, increasing interventions, with ethnicity and deprivation also being significant but having variable impact and direction. The effect of mothers’ age varied by indicator. For nine indicators the best outcome was achieved in the age group ≤ 19 years and the poorest outcome in mothers aged ≥ 45 years for all indicators except intact perineum, where the relationship was U-shaped. The proportion of elective caesareans increased with age and this trend was similar but less strong for emergency caesareans.
Higher clinical risk was associated with fewer mothers achieving various healthy outcomes. The effect size (relative chi-squared value) for clinical risk was highest for normal birth and all caesareans and weakest for intact perineum. Analysis using the three-category clinical risk variable found that women in the individual assessment group nearly always had better outcomes than women in the higher-risk group based on NICE guidelines; the one exception was intact perineum.
There were improved outcomes with increasing parity for six indicators (healthy mother, healthy mother/healthy baby dyad, delivery with bodily integrity, normal birth, spontaneous vaginal delivery and intact perineum). For a number of these indicators there were marked improvements in outcomes between nulliparous mothers and mothers who already had one child. Healthy baby outcome and all caesareans were less affected by parity than the other indicators. There was a sharp increase in the proportion of elective caesareans between nulliparous mothers and mothers of parity 1 and the reverse for emergency caesareans.
Ethnicity and deprivation had their strongest associations with delivery with bodily integrity and intact perineum. Caribbean (black and black British), and white and black Caribbean mothers (mixed race) were the most likely to deliver with bodily integrity and have an intact perineum, and Indian and Chinese mothers were the least likely. For eight indicators, the more deprived mothers had better outcomes. In comparative terms, the healthy baby outcome did not vary greatly by ethnicity, while deprivation had less of an effect upon all caesareans.
Bragg et al.39 in a cross-sectional analysis explored whether or not variation in unadjusted rates of caesarean births could be explained by maternal characteristics and clinical risk factors. 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, suggesting that organisational factors would need to be included in future analysis. This work was furthered by the RCOG in its Clinical Indicators Project,25 which identified 11 potential performance indicators derived from HES data. This report similarly found unexplained variation across trusts. Organisational factors such as trust configuration, size, models of care, staffing levels and skill mix remained unknown factors and we explore these in the next section.
How do organisational factors affect variability in maternal interventions and maternal and perinatal outcomes?
Approximately 1–2% of the total variation in the outcome indicators was attributable to differences between trusts, whereas 98–99% of the variation was attributable to differences between mothers within trusts. There was marginal improvement in a model’s capacity to predict outcomes following the addition of sociodemographic, trust-level and staffing variables. Based on the AUC, most models meet the criteria for fair to good prediction. The key factors are summarised in the next section.
Healthy mother and healthy baby
The results for the three healthy mother and healthy baby indicators (healthy mother, healthy baby, healthy mother/healthy baby dyad) are summarised. The overall fits of these models based on the AUC were all in the range 0.70 to 0.80 (fair).
For mother and baby indicators, and the healthy mother/healthy baby dyad, clinical risk was a strong and dominant predictor, then mother’s parity with moderate effects for mother’s age and IMD. The relationship with parity was linear for the healthy mother indicator, with mothers more likely to achieve a healthy outcome with increasing parity.
The chance of a healthy mother outcome was negatively associated with deprivation. Mothers belonging to the most deprived IMD quintile were more likely to achieve a healthy mother outcome than mothers belonging to the least deprived quintile. This relationship was reversed, and less strong, for the healthy baby indicator. There was some variation by SHA and rural/urban classification, although the size of these effects was of a much lower order of magnitude than for mothers’ characteristics. For the healthy mother/healthy baby dyad the outcome was achieved least often in London.
Mothers attending a university hospital trust were less likely to give birth to a healthy baby. Size of trust had no impact, although there was a negative effect (i.e. the more births in a trust the poorer the outcome) that approached statistical significance for the healthy mother indicator. This may be because of sick mothers and babies being referred to large units, skewing their proportions of healthy mothers and healthy babies, although clinical risk has been controlled for in these models.
Mode of birth
The results for the four mode of birth indicators are summarised. The overall fits of these models based on the AUC were all in the range 0.70 to 0.80 (fair). Parity and clinical risk were the two dominant predictors of outcome. The effect of increasing parity increased the chance of a delivery with bodily integrity, a normal birth, a spontaneous vaginal delivery and an intact perineum. Being at increased clinical risk of complications during the birth reduced the chances of these outcomes.
Women living in more deprived areas were more likely to deliver with bodily integrity, have a normal birth, experience a spontaneous vaginal delivery and have an intact perineum. The effects of geographical location, defined by SHA, and type and density of the area in which mothers lived were of a much lower order of magnitude than the effects of mothers’ characteristics.
Giving birth in larger trusts with more deliveries lowered the chances of delivery with bodily integrity and an intact perineum. For spontaneous vaginal delivery, the outcome was better in trusts not attached to a university but again the effect was small. Trust configuration, i.e. whether or not it had midwife-led units, appeared to have no effect upon mode of birth outcomes.
Caesareans
The results for the three caesarean indicators are summarised. The overall model for elective caesareans achieved a noticeably higher AUC statistic than for emergency caesareans and therefore was far better at predicting the outcome. The chances of a caesarean were lowest for mothers aged ≤ 19 years, rising thereafter with increasing age. Women living in the most deprived areas were less likely to undergo an elective caesarean than those living in the least deprived areas. The relationship is reversed for emergency caesarean and where the effect of deprivation was less strong. There was some variation across SHAs for all caesareans, with women living in London most likely to have a caesarean.
Summary
Variation between trusts represents a comparatively small component of the overall variation (approximately 1–2%). This variation was not substantially reduced by the addition of the independent variables, although reduction was more evident amongst the mode of delivery indicators.
The funnel plots suggest that there was more variability than expected by chance, with more data points outside the control limits. The funnel plots generally confirmed what was found in the multilevel models, based on the change in the residual sigma estimates, that the models did not reduce the variability between trusts to any great degree.
Potentially there is capacity to improve the fit of these models by adding further variables, although a number of the variables that might help in this respect were inadequate for use in the analysis (e.g. smoking, body mass index) because they were either poorly or not consistently recorded.
Overall, the effects of trust size and university status were small. It was more often the case that larger trusts performed less well than smaller trusts. Giving birth in a larger trust reduced a woman’s chances of having an intact perineum, giving birth with bodily integrity and having a healthy mother or healthy mother/healthy baby outcome. When the analysis was restricted to trusts with a single OU, the analysis also showed that women were more likely to have an emergency caesarean in a larger unit and less likely to have a normal birth. University trusts seemed to perform as well as, and often better than, their non-university counterparts in the restricted analysis.
What is the relationship between maternity staffing, skill mix and maternal and perinatal outcomes?
Overall, the linear effects of the staffing variables were not statistically significant for eight indicators. However, all women benefited from an increase in midwifery staffing, in terms of retaining an intact perineum and bodily integrity.
Looking at mode of birth indicators, a higher number of midwives (FTE per 100 maternities) and higher levels of overall staffing were associated with improved chance of delivery with bodily integrity and an intact perineum. There was a reduction in the between trust variation across all four mode of birth indicators, particularly for normal birth, when mothers’ characteristics, sociodemographics, trust-level and staffing variables were added to the intercept-only model (i.e. the model that contains no independent variables). Staffing variables had a non-significant effect upon the chances of a caesarean.
We then investigated whether the effect of staffing levels upon outcomes could vary according to either a woman’s parity or her clinical risk. An analysis of the multiplicative effects of parity and clinical risk with the staffing variables was more revealing.
Risk status: doctors
Higher-risk women were more likely to have an increase in spontaneous vaginal delivery rates and reduced elective caesarean rates with an increase in doctors. Lower-risk women were more likely to remain healthy throughout birth with higher numbers of doctors.
In the presence of higher numbers of doctors, however, lower-risk women were more likely to have an elective caesarean and less likely to have spontaneous vaginal delivery.
Risk status: midwives
Lower-risk women were more likely to have an increase in healthy mother, healthy baby and healthy mother/healthy baby dyad outcomes with an increase in midwives, and less likely to have an emergency caesarean.
Risk status: support workers
Lower-risk women were more likely to have an increase in normal birth, intact perineum and bodily integrity and lower emergency caesarean rates with more support workers. However, women were less likely to have a healthy mother outcome whatever their risk. Higher-risk women were less likely to have a healthy mother, healthy baby or healthy mother/healthy baby dyad outcome.
Parity: doctors
With more doctors, nulliparous women and women with parity of ≥ 4 were less likely to have an emergency caesarean, and multiparous women were less likely to have an elective caesarean. Women with higher parities were more likely to have higher spontaneous vaginal birth rates as the number of doctors increased and nulliparous women were more likely to have healthy mother, healthy baby and healthy mother/healthy baby dyad outcomes, although rates decrease as parity increases.
Parity: midwives
With more midwives, women in the higher parities were more likely to deliver with an intact perineum and nulliparous women were more likely to have an increase in elective caesareans.
Parity: support workers
The level of support workers appeared to have little significant effect based on parity, although intact perineum rates were more likely to be increased for women with parity > 2.
Differing staff levels and configurations may have an impact on outcomes of quality and safety. Trusts that have higher levels of midwife staffing are more likely to have higher numbers of nulliparous women having elective caesareans. Increasing the number of doctors appears to have most benefit for women at higher risk of complications, but does not benefit lower-risk women. More doctors improve the chance of healthy outcomes in nulliparous women who labour, and midwives have the most beneficial effect when looking after low-risk women. Support workers are also best deployed with low-risk women, reducing intervention rates. However, caution needs to be exercised with any increase in number and deployment of support workers, as they can also have a negative impact on the healthy mother and healthy baby outcomes in all groups.
Murray et al.77 found no significant differences in results between hospitals with high- and low-quality coded HES data, suggesting that hospitals with high birth record completeness may be generalisable and representative of all hospitals. However, caution should be exercised regarding relevance to other countries and health-care systems. The small relative effect of staffing may be due to limited variation in staffing levels and skill mix. This may be due to the influence of guidelines regarding medical and midwifery staffing. In countries with wider variations, staffing may be shown to have a greater effect.
What is the relationship between maternity staffing, cost and outcomes?
We examined the relationship between maternity workforce staffing levels, quality and safety outcomes and cost. Data on medical staffing were not detailed enough to ascertain the split between obstetric and gynaecological responsibilities in a trust. Therefore, this analysis investigated the relationships between midwifery staffing levels, where data quality was very good. For this analysis we used trust-level data to investigate relationships between outcome measures, midwifery staffing levels and the cost of providing maternity services for NHS trusts in England.
When the case-mix adjustments were included in the model there was no relationship between antenatal spend and reduced operative delivery rates and lower delivery costs. No relationship was found between the proportion of maternity expenditure spent on antenatal care and operative delivery rates after adjusting for maternal characteristics and trust size.
Higher midwifery staffing levels were associated with higher costs of each delivery, although the relationship was not strong. Only around 17% of the variation between trusts’ delivery costs could be accounted for by variables included in this model. The remaining variation in the average cost of a delivery was not accounted for by maternal characteristics, size of trust, number of FTE registered midwives employed or antenatal spend and must be due to other factors not included in the analysis.
The analysis found that higher operative delivery rates were not significantly associated with higher delivery costs after adjusting for maternal characteristics. Variations in costs between trusts were not related to the numbers of women having operative deliveries.
There was no association between delivery cost per delivery and the normal birth rate. There was no association between delivery cost per delivery and the intact perineum rate, or any of the three healthy mother and healthy baby indicators, and women’s experience of maternity care as measured by the average of the CQC scores. A relationship could not be found that explained postnatal costs in terms of variations in operative delivery rates once adjustments were made.
Having a higher proportion of women at increased risk of complications was associated with more expensive maternity care. Level of social deprivation approached significance with deliveries in trusts, with a higher proportion of women living in deprived areas having more costly deliveries. However, in this analysis trust size and case mix accounted for only 22% of the variation in total delivery costs. Some of the previous analyses also showed relationships between costs and maternal characteristics. For trusts with a larger proportion of women at increased risk of complications, delivery costs were higher and they spent a higher proportion of their total maternity costs on postnatal care, and trusts with a higher proportion of nulliparous women had higher costs per delivery.
Larger trusts, measured by the numbers of deliveries, appeared to offer maternity services that cost less than those in smaller trusts. Repeating the analysis for the 50 trusts that only consist of a single OU, the significant effect of size increased, suggesting that the economies of scale were greater in a single site. The size of the trust had no relationship with the cost of a delivery only (i.e. when costs of antenatal or postnatal care were excluded). Deliveries were not cheaper in larger units once adjustments had been made for maternal characteristics and numbers of midwives. This lack of relationship held when repeating the analysis for the 50 trusts that delivered maternity services through only a single OU each.
Larger trusts were associated with lower CQC scores for women’s experience of maternity care. There was no relationship between trust size and the other outcome measures: normal birth, intact perinea and number of complaints.
From this study, the increased investment in staff did not necessarily have an effect on the outcome and experience measures chosen, where there was in general no relationship with midwifery staffing levels. However, there was a higher intact perineum rate in trusts with higher levels of midwifery staffing. Although this validates the result to some extent, any trusts submitting erroneous data will be correlated between years. It would be interesting to see if the relationship holds good if the data are analysed at individual record level. Maternity units with higher levels of midwifery staffing may find it easier to provide continuity of carer and one-to-one midwifery care. This finding would then be consistent with research evidence that one-to-one midwifery care can result in a significant reduction in perineal trauma.70 This is an important outcome for women which impacts on the quality of their life with a new baby and could impact on future decisions about mode of birth.96
Although there was no significant improvement in women’s experiences of care, as measured by CQC scores, as a result of higher staffing, there was a trend in that direction. These scores were not all directly related to midwifery staffing and covered a wide range of women’s experience of care.
While overall levels of midwifery staffing are important, other factors will also affect outcomes and experience of care for women. The deployment of those staff within the maternity service, their attitude towards the women they care for, their skills and the culture within which they work will all play a part in women’s care.
Despite the relationship of higher levels of midwifery staffing with improved intact perineum rates, and the higher costs associated with providing that staffing, when tested directly there was no relationship detected between delivery costs and improved perineal outcomes. In fact, few patterns connected the cost of providing maternity services with differences in the populations they serve or the complexity of births. Higher operative delivery rates were not associated with higher costs of intrapartum care (after adjusting for case mix) and this emphasises the inherent variability found in the reference costs, as found by Laudicella et al.67 Excess bed-days were not included with delivery costs in these analyses (instead being included in postnatal costs). It may be that including the extra duration of stay following an operative birth would have an impact on this relationship,68 although this is unlikely, as the cost of excess bed-days was very small in relation to total cost. Trusts with higher numbers of women at increased risk of complications do appear to be associated with higher delivery costs, as found by Gaughan et al.,68 and also with higher postnatal costs. This pattern is reflected in the new maternity pathway system, where higher payments are made for higher-risk women, but interventions themselves are not rewarded.
Providing more costly antenatal care alone did not appear to have a direct impact on operative delivery rates and was not recouped by a commensurate reduction in delivery costs, nor did it result in a better experience of antenatal care as measured by the CQC antenatal summary score. Some trusts did appear to be able to increase antenatal expenditure and reduce delivery costs. However, these trusts may be attributing costs differently between antenatal spend and delivery cost, which would create the illusion of a trade-off. From the data it is not possible to decide if these trusts have a winning formula.
The analyses generally adjusted for differences in size of trust as measured by the annual number of deliveries. The biggest effect of trust size was seen with total costs, particularly when restricted to trusts which had a single site only. Costs per childbearing woman were lower for larger units after taking into account case mix, which may have been a result of economies of scale. As no relationship could be seen between trust size and delivery cost alone, it could be deduced that the differences are related to antenatal or postnatal costs. However, the wide variations in costs suggest that trusts may be allocating costs differently along the maternity pathway.
Women seemed to be slightly less satisfied with their experience of care, as measured by CQC scores, in larger trusts than in smaller ones. Again, because of the complexities of trust configurations, it may be that size of unit is more important than size of trust, but CQC scores are available at trust level only. Although larger trusts appear to spend less overall, and may offer a lower quality of service from the point of view of women’s experience, this type of analysis does not show causality and therefore it is not possible to assume that spending less on maternity care results in lower-quality care.
Relationships between staffing, costs and outcomes are complex and it is perhaps unsurprising that there was often no clear correlation between these different variables. However, these results do not contradict the idea that quality of care is not directly related to the costs of providing the services and that quality and cost do not need to require a trade-off. We explore this in the economic modelling.
Economic modelling
The economic analysis was frustrated by the data limitations described in the next section, mainly with respect to the availability of detailed staffing data. Despite this, a number of interesting findings emerged from the economic modelling, which were consistent across the two definitions of maternity output: the total number of deliveries per trust and the cost-weighted deliveries per trust. The latter definition was an attempt to control for the greater complexity and resource use involved in performing caesarean births, which is accounted for in the higher reimbursement received by trusts.
To accommodate this, the cost-weighted deliveries combined these two broad types of deliveries based on their relative cost. However, the results were very similar across both models and we can therefore discuss them together. The Hick’s elasticities of complementarity were estimated. This measures the extent to which staff groups are complements or substitutes, i.e. whether they should be used in combination or can be used in place of each other in terms of the number of deliveries a provider can handle each year. This measure indicated that midwives were complements with consultants and other doctors in the production of deliveries, that is, midwives and doctors and midwives and consultants should be used in combination to maximise the number of deliveries a provider handles. Consultants and other doctors were found to be substitutes for each other, as were midwives and support workers. In that sense, there are some tasks that a consultant and more junior doctor can both do, as there are tasks that both midwives and support workers can perform. This echoes findings from research by Goryakin et al.62 that looks at the relationship between registered nurses and health-care assistants. The elasticities in Table 34 indicate the degree to which staff groups are either complements or substitutes and none of them imply that you simply trade, for example, one FTE midwife for one support worker.
As described in the literature review, this is the first study that we are aware of to look at this issue and, therefore, there are no appropriate studies with which to compare our findings. Thurston and Libby65 employed a similar methodology but applied to primary care physicians in America. They found that all staff groups were complements except for technicians, who were substitutes for both nurses and administrators. Our findings are similar in that midwives and support staff are also found to be substitutes, while most other groups are found to be complements. We also find that consultants and other doctors are substitutes for each other, which was not an issue considered by Thurston and Libby65 because they considered primary care practices and the grade or experience of the physician was not modelled.
The marginal products of all staff groups were positive, indicating that adding an additional worker of any type would increase the total number of deliveries a provider would be able to cope with. Purely in terms of the number of additional deliveries and not considering the cost of adding an additional worker, doctors had the highest marginal productivity followed by consultants, then midwives and finally support workers. Adding an additional FTE worker in each of these categories would allow a hospital to handle an additional 43, 32, 18 and 10 deliveries per annum, respectively. Conversely, reducing staffing by one member of staff in each group would reduce a hospital’s output by an equivalent amount. Given that the national average number of deliveries per FTE in these staff groups respectively is 180, 384, 32.5 and 100, the marginal productivities support the results that most staff groups are complementary, i.e. they should be used in combination. Adding additional workers of one category (e.g. midwives) will not be as productive as adding a combination of all the staff groups: the right skill mix is therefore critical to the efficient operating of a maternity service. However, substituting support workers for midwives may also have an impact on some aspects of the quality of care depending on the groups of women involved or the care setting.
Limitations
Secondary analysis is dependent on the quality of data. We used the full census of women’s deliveries in HES (656,969 delivery records) so there was no bias caused by non-response. Any biases would therefore be caused by missing data, poorly recorded data or omitted variables from the risk adjustment model. A scoring system was used to select records with the largest amount of most useful and relevant data of greatest relevance to the project. Extensive data cleaning was conducted to remove duplicates and records which did not relate to a delivery episode, identifying units with inconsistent or missing data. A decision was taken not to include any trust where fewer than 80% of women could be coded for a particular indicator. This limited the use of some potential indicators.
A common problem when analysing routinely collected data is that it is only possible to work with the available data that are of a sufficient quality to use. For example, we could not include body mass index or an indicator of smoking status in our models because of data quality issues, although they are known to be important risk factors. There may well be other measures of clinical well-being and lifestyle that go beyond the NICE risk classification that would help to reduce variability.
Only a limited set of trust-level organisational variables were used. We were not able to include measures that tell us something about the organisation (e.g. organisational climate, local climate) and models of care that could be predictive of outcome. Our models may also have omitted other variables, either known or unknown, that are predictive of outcome.
Staffing data were available only at trust level so we could not explore the effects of staffing at the unit level. The data for trusts that have multiple units could not be disaggregated. Aggregated trust-level data makes the assumption that unit-level effects within a trust are similar, which may not be true. The staffing data are taken from a census undertaken every September. This single-point estimate will hide any fluctuations that may occur over time. We analysed data that were aggregated over a period of a year. These data will therefore miss those occasions when the service is placed under stress, or reaching a critical point, because of excess deliveries, low staffing levels or other factors.
This study, like many others in the literature, relies on a single cross-section of data that makes causal inference problematic if not impossible. At best we can claim an association between our independent variables and the outcomes. An obvious concern is omitted variable bias. Some potentially omitted variables have been listed above, including inter alia smoking status (for mothers) and a number of trust level variables. It is possible that trusts with higher staffing levels also have higher levels of other inputs that affect organisational performance and the quality of care, such as advanced medical equipment, high performance management teams or a culture of patient safety. A major problem involves the potential endogeneity between staffing levels and the outcomes we have used. In effect, trusts make decisions about factors that have an impact on the quality of care, for example staffing, subject to a set of constraints such as regulation, limited budget and case mix.
Adding additional years of data would allow for some control over unobserved variables that vary across providers but do not vary over time. However, only in an experiment that deliberately (or fortuitously as the result of a policy design) allows for the manipulation and randomisation of staffing levels could researchers make casual claims about the relationship between staffing and outcomes. Therefore, this concern is not unique to this study.
Limitations of cost analysis
This investigation brought together data from a wide variety of sources, including trust profiles and outcome data from an extensively cleaned HES data set. The analysis included measures of women’s experience of care.
There are a number of limitations to this analysis. Firstly, the analyses were done using data aggregated at trust level, rather than at the level of individual patient records. This analysis, because of data limitations, has considered only the number of registered midwives and not taken into account the use of maternity support workers, nor has it considered the medical workforce or nursing staff working alongside midwives.
Reference costs and staffing data were available only at trust level, and not for individual maternity sites within trusts. There were a wide variety of configurations of maternity services with trusts operating one or more obstetric sites and varying numbers of AMUs and FMUs, and differences in costs, staffing and outcomes between these units may have masked some associations. Because of this, some analyses were undertaken using trusts that operate only a single obstetric site. Extreme caution needs to be exercised regarding the use of reference costs versus actual costs.
Ethnicity was not included as a variable in this analysis. As a categorical variable with a large number of potential coefficients, its inclusion would have been detrimental to the determination of the other coefficients. Grouping ethnicity would have reduced the number of coefficients, but ethnicities that seem similar have different outcomes (e.g. Indian and Pakistani women). As ethnicity is only a minor driver of outcome, it has been excluded from the analyses.
Limitations of the economic modelling
The economic analysis was limited by a number of, primarily, data-related factors. While the quality of the workforce data has steadily improved since the introduction of the Electronic Staff Record system, the data remain limited for this type of research. The data were reported at trust rather than unit level, and there was no account for time spent in different roles or departments (e.g. obstetric vs. gynaecology). There were also limitations in the availability of data on bank and agency staff used.
It was not possible to obtain credible data on the other inputs such as capital and variable inputs such as drugs. Therefore, it was not possible to investigate any input substitution between, say, capital and labour. Given the focus of this study on skill mix, this was less of a concern for the research team.
Finally, the functional form adopted for the production function analysis induces multicollinearity in the variables due to the inclusion of cross-products to test for substitution and complementarity of inputs. Given the relatively low degrees of freedom in the models resulting from a small number of trusts (144) in the data set, few of the variables were statistically significant. It is not possible to ascertain whether this was due to the high degree of multicollinearity or the lack of a statistically significant relationship in the data. Yet as there was a high R-squared value, it is likely that multicollinearity is the cause and that the variables are indeed significant predictors of output. There are few options to resolve this issue because it is not possible to collect additional data on trusts, nor is it appropriate to reduce the functional form to a simpler specification such as a Cobb–Douglas production function.
Public and patient involvement
This project has had PPI at every stage of the research project, from the development of the proposal, through undertaking some of the research, providing an advisory role, drafting and commenting on the report, to dissemination of the findings.
As a result the findings will be relevant not only to clinicians, policy-makers and NHS managers, but to pregnant women and their families. A particular impact has been on the choice of indicators used as measures of the quality of maternity care. These include a number of positive measures, rather than just a series of interventions or harms. These have included existing measures such as normal birth. This used a consensus definition agreed by the Maternity Care Working Party,17 but based on a definition originally proposed by the user organisation BirthChoiceUK. Further innovative positive measures which indicate an absence of physical harm are those of intact perineum and delivery with bodily integrity. Being without damage or sutures in the early postnatal days enables women to recover more quickly from the birth and to feel more physically comfortable as they begin the tiring work of looking after a new baby. A trio of further measures reflect the health of the mother, the baby and the mother and baby dyad. These combine the concepts of safety, clinical effectiveness and women’s experience to view how the well-being of both the mother and baby as they emerge from the birth process, whatever that may have been.
Our lay collaborator was also able to undertake part of the research herself, taking on responsibility with the obstetrician (SB) for identifying codes in women’s HES records which might indicate that a women would be considered to be at increased risk of complications for birth. This proved to be one of the most important characteristics in determining outcomes for women, according to the multilevel model, along with parity. This work has implications for future analyses, where outcomes can be stratified by risk and parity, and potentially provide women with more personalised information about their likelihood of particular outcomes.
One of the challenges of involving recent service users in this study has been that it has largely been a paper exercise, analysing routinely collected data and understanding the results of multilevel logistic regression, together with the selection of quality indicators. For someone familiar with quality metrics and HES maternity data and with a basic statistical understanding, this has not been as challenging as it might have been to a less expert user.
Although joining the project team and undertaking some of the research herself has brought benefits to the project, it has required a time commitment that might have been difficult for other service user representatives. In this case, the project was not able to provide full funding for this (as the time commitment and contribution were recognised only once the project had started). However, it is pertinent to consider for the future how such time should be costed in for ‘expert patients’. They may have little academic record but a wealth of experience and generally go unfunded. Universities must consider how to support payment to such self-employed individuals within an increasingly tightly regulated environment.
There also appears to be a trend for those reviewing grant proposals to discount the PPI contribution of experienced service users, and user-researchers who work independently. While having recent service users with a variety of experiences and sociodemographic backgrounds is clearly important to many projects, experienced service users can bring a wider, less personal perspective, bringing together views of many consumers, and often with a knowledge of the research literature and policy from a patient perspective also. It is vital that this not be undervalued by reviewers of grant proposals.
