Right cot, right place, right time. Using neonatal care data and computer simulation to improve the design and organisation of neonatal care networks.

Article history

The research reported in this issue of the journal was funded by the HS&DR programme or one of its preceding programmes as project number 10/1011/48. The contractual start date was in January 2013. The final report began editorial review in June 2014 and was accepted for publication in January 2015. The authors have been wholly responsible for all data collection, analysis and interpretation, and for writing up their work. The HS&DR editors and production house have tried to ensure the accuracy of the authors’ report and would like to thank the reviewers for their constructive comments on the final report document. However, they do not accept liability for damages or losses arising from material published in this report.

Declared competing interests of authors

Sue Prosser is a senior neonatal nurse at the Royal Devon and Exeter Hospital.

Copyright statement

© Queen’s Printer and Controller of HMSO 2015. This work was produced by Allen et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

Neonatal care data

Neonatal care data were extracted from Badger data (www.clevermed.com/). Badger is the proprietary patient data management system used in all the hospitals in this study. Badger records the date, time, origin and reason for admission, and the date, time and destination of discharge (including if an infant is discharged for specialist surgical or cardiac care). For each day of care the interventions received are recorded and Badger then classifies an infant by care level according to BAPM 2001 or 2011 guidelines7 [the differences between 2001 and 2011 categories of care have been summarised by the neonatal data analysis unit (NDAU)19]. Appendix 1 gives an example of Badger data used for analysis and as the basis of modelling. The model was based on 2 years of admissions in Badger (January 2011 to December 2012 inclusive). In this period there were 6197 Badger episodes (a Badger episode occurs for each admission to a hospital) and 5665 unique infants (7.1% of infants had more than one episode). A subsequent 6 months of data was used to test and validate the model.

Nurse staffing

Information on nurse staffing came from a daily ring-around survey of the number of nurses present in the units. The daily ring-around involves the nurse in charge at each site. The numbers of clinical on-duty staff are recorded for day and night shifts, separated by those who are qualified in specialty and those not. These numbers exclude supernumerary staff such as managerial staff (including the nursing team leader if he or she is not on clinical duty, student nurses and staff involved in training/teaching). Staff who are rostered to be present but who are absent (e.g. because of illness) are not counted. Although there is good standardisation of the data collected, a limitation is that the reality may sometimes be more complicated. For example, many units may have a supernumerary team leader, especially during the day; the ring-around audit data assume that the leader will not be available for clinical duties, but if the unit is highly stretched the supernumerary nurse may lend a hand. We used the day shift numbers excluding supernumerary headcount and excluding one nurse dedicated to neonatal transfer duties. The data used were from January 2011 to December 2012. Our analyses do not seek to identify availability of nurses throughout a day: we have assumed that, if a nurse is present for a shift, then he or she meets the availability requirements to count as being able to care for infants throughout that day (e.g. one nurse would meet the standard of 1 : 1 care for an infant in IC throughout his or her shift, although there may be inevitable breaks in availability throughout that shift).

Travel distances and travel times

Travel distances and travel times were obtained using Microsoft MapPoint version 2013 (Microsoft Corporation, Redmond, WA, USA) using the MPMileCharter add-in (version 3.4, Winwaed Software Technology LLC, Dallas, TX, USA) to allow calculation of large matrices of routes. Travel times and distances are fastest routes by road unless otherwise stated. Public transport times were obtained using the Transport Direct search engine (www.transportdirect.info) run by a non-profit consortium appointed by the Department for Transport. See Chapter 6 for more details.

Demographic and births data

Demographic data for deprivation were obtained from the Office for National Statistics (ONS). 20 These were mapped using Google Fusion Tables (Google Inc., Mountain View, CA, USA). The number of women of child-bearing age (15–45 years) and the fertility rate of that age group were obtained from NHS England National General Practice Profiles. 21 The number of births in England was obtained from the ONS22,23 or from NHS Hospital Episode Statistics (HES), queried using the Signals From Noise tool from Lightfoot (version 8.9, Lightfoot Solutions Group Ltd, Bracknell, UK).

Ethical review

This project used anonymised secondary data only. Access to neonatal data (Badger) was approved by Proportionate Review by the Research Ethics Committee (REC Reference 12/SC/0675) and local Caldicott Guardian approval (approval obtained from Plymouth Derriford Hospital, the single point of access to all neonatal data used).

Microsoft MapPoint

For analysis and modelling of travel distances and times we have used Microsoft MapPoint. We used the MPMileCharter add-in to allow calculation of large matrices of routes.

Road distance and time versus straight-line distances

The current model uses fastest-route road distances and times between patient nodes and hospital locations. We have, however, looked at the feasibility of using straight-line distance should the model be expanded significantly. At a national scale we predict that there are 1.3 million routes to identify (routes from each GP location to all neonatal units). We estimate that this will take about up to 30 days of processor time to calculate using Microsoft MapPoint. Straight-line distances are rapid to calculate (less than 1 hour for all 1.3 million national routes). Two hundred postcodes were selected at random in England and straight-line distances, fastest-route road distance and fastest-route travel time between all postcodes were obtained from MapPoint (20,000 routes in total). We found a very high correlation (R² = 0.98, Figure 1) between straight-line distances and road distances, with road distances on average being 24% greater than straight-line distances [median 25%, interquartile range (IQR) 20–32%]. Travel time correlated well with straight-line distance (R² = 0.97), although the correlation was not as close as with road distance (R² = 0.99).

FIGURE 1.

Comparison of straight-line distances, road distance and travel times (20,000 routes). (a) Comparison of road distance (fastest route) against straight-line distance. (b) Comparison of predicted travel time against straight-line distance. (c) Comparison of predicted travel time against road distance (fastest route).

Public versus private transport

A comparison of public and private transport times was made using the Transport Direct search engine run by a non-profit consortium appointed by the Department for Transport.

Public and private travel times were compared for all journeys in Devon and Cornwall (the 220 patient nodes to the five hospitals). The search engine returned both private and public transport times for 99% of all the possible combinations. Results are shown in Figure 2 and summarised in Table 1. On average, use of public transport takes twice as long as travel by car. We have generally assumed that a distance of 50 km (taking approximately an hour by car) represents a reasonable maximum travel distance, but it should be noted that parents relying on public transport may face significantly more time travelling, or may depend more on accommodation local to the neonatal unit. The distance able to be travelled using public transport, before local accommodation is required, is therefore likely often to be less than the acceptable distance for a private car driver. Feedback was received from a parent involvement focus group that this may occur if parents do not have access to a car that may be dedicated to travel to a neonatal unit, or may occur after a caesarean section.

FIGURE 2.

A comparison of public and private travel times between patient locations and hospitals in Devon and Cornwall. (a) Comparison of public transport times against private car travel. (b) Comparison of travel time against distance for private car (open circles) or public transport (open squares).

Analysis framework in R

Preparation and analysis of neonatal transition and length of stay (LoS) data was performed in the statistical computing language R version 3.0.2 (The R Foundation for Statistical Computing, Vienna, Austria). Additional analysis of Badger data was performed using Excel 2010, Microsoft Corporation, Redmond, WA, USA.

A bundle of R code was developed to carry out various analysis tasks. The code can work with both Badger data and output from Simul8 2013 (Simul8 Corporation, Boston, MA, USA) models. For example, it can be used to analyse lengths of stay, or for analysing transition probabilities between different levels of care (with or without analysis by gestational age group).

The tool is intended to be applicable more generally than in this project alone, so it:

• is, in principle, able to process other or larger data sets in the Badger format (is ‘scalable’)

• has been incorporated (along with relevant documentation and data) into an R ‘package’ called ‘Neonatal’ which can be shared with other analysts using R (an R ‘package’ is a standard way of collecting together code and data in a form that can be shared; many are available online at http://cran.r-project.org/).

The aim is that analysis tasks can be carried out in an interactive way with fairly simple commands, even though the analysis tasks themselves can be lengthy and involved. The R package takes four sets of data for its analysis (Figure 3):

FIGURE 3.

Combination of four data sources into a single Badger object.

• Badger data (from a standard Badger query, summarising lengths of stay in each episode for each infant)

• workforce data (number of nurses on duty each day shift, where available)

• demand location data (e.g. postcodes of mothers’ GPs)

• travel distances matrices: fastest route distances between each demand node and hospital and between each hospital (data from Microsoft MapPoint).

The R package pre-processes the Badger data (Table 2) and merges with the other data sources (workforce, demand locations, distance matrices) to create a single R object of type ‘Badger’.

Following pre-processing, the Badger object contains a number of components as outlined in Table 3.

Analysis tasks can then be carried out on a Badger object in an interactive way with fairly simple commands, as indicated in Table 4. These commands can contain further options for finer control, such as restrictions on the time period of the data. Furthermore, many of these tasks can be ‘stratified’ so that analysis is split by other factors of interest (most likely age category or location of care).

Quantification of workload and lengths of stay in each care level

Note: Segregation into IC, HDC, SC or TC is by BAPM 20117 standards unless otherwise stated. All data refer to admissions in 2011 and 2012.

Infants in different levels of care require different levels of nursing according to DH and BAPM guidelines. 3,7 This is described in Table 5.

‘Workload’ in this report is the calculated number of nurses required to look after the infants present in the unit (Equation 1). A simplification in the simulation model is that requirements for different types of nurses are not determined; it is assumed that there is an appropriate skill mix of nurses for any particular unit (the skill mix is taken into account in economic analysis in Daily variation in case mix, workload and nursing numbers).

Equation 1: Workload calculation. Workload is the number of on-duty nurses required to care for the number and mix of infants present in the unit. Workload excludes supernumerary nurses (e.g. nurses dedicated to management or neonatal transfer).

Badger data are summarised for each day of care. Total Badger care days add up to more than the actual LoS, as any part day is counted as a full day in Badger. We therefore adjusted the days in each level of care in proportion to the total LoS calculated from admission and discharge times (see Figure 4).

FIGURE 4.

Calculation of lengths of stay in different care levels. In Badger any part day at any level of care is recorded as a full day. Lengths of stay in each care level are adjusted to be in proportion to total LoS.

Location of demand

There are 220 GPs whose closest hospital is one of the five hospitals in our main study area. GPs are, on average, 2.9 km apart by straight-line distance (median 1.0 km, IQR = 0.4–4.8 km, distances calculated using Microsoft MapPoint with MPMileCharter add-in). GPs are, on average, 3.6 km apart by road (median 1.5 km, IQR = 0.6–6.0 km, distances calculated using Microsoft MapPoint with MPMileCharter add-in).

We extended this analysis to national distances between GPs. There are about 8000 GP practices in England, which on average are 1.4 km apart by straight-line distance (median 0.6 km, IQR = 0.3–1.3 km, distances calculated using Microsoft MapPoint with MPMileCharter add-in, practice locations from Public Health England Practice Profiles). This is in close alignment to a study in the South West of England which calculated the median straight-line distance from a patient’s home location to their GP as 1 km (IQR = 0.6–2.0 km), with 95% of electoral wards (98% of the population) being under 4.4 km, or 6.3 minutes, from their closest GP. 27

Figure 5 identifies locations of GPs and hospitals. Figure 6 shows demand by GP location as a heat map. Unsurprisingly, demand is highest around the main urban conurbations.

FIGURE 5.

Location of GPs (patient nodes, yellow circles) and hospitals (red flags) in the Peninsula region. © 2015 Google – Map data © 2015 Google.

FIGURE 6.

Heat map of demand (births requiring care in a neonatal unit) by GP location in the Peninsula region. Red locations indicate the highest density of demand. © 2015 Google – Map data © 2015 Google.

Ninety-five per cent of infants were cared for, from birth to discharge, in the Peninsula region. Of those going outside the network, 52% went to a specialist surgical or cardiac unit.

Comparison of demand with births

For GPs whose closest hospital is one of the five hospitals of the main study area, there are a total of 17,753 births predicted per year (based on GP practice fertility rates and the number of women aged 15–45 years at each GP; data from NHS England). 21 This compares with 2012 statistics from the ONS of 18,186 live births across Devon and Cornwall. 23 Data from Badger showed 2580 admissions in 2012 from GPs whose closest hospital is one of the five hospitals of the main study area. Admission rates into neonatal units are therefore 15.5–16.0% (depending on the source of the number of births). Admission into IC was 386 births (based on BAPM 2011 guidelines) or 715 births (based on BAPM 2001 guidelines) in 2012, corresponding to admission rates into IC of 2.1% or 3.9% depending on which BAPM guidelines were used.

Figure 7 shows the correlation of admissions per GP with all births or with low birthweight (< 2500 g) births. Admissions correlate well with both indices (R² 0.70 and 0.65 respectively). The number of births and low birthweight births by general practice are available from NHS England. 21 Using births per GP provides an alternative input for later modelling.

FIGURE 7.

Correlation of neonatal admissions with births or low birthweight births per GP. (a) Comparison of the number of admissions into neonatal units from each GP with the total number of births per GP. (b) Comparison of the number of admissions into neonatal units from each GP with the total number of low birthweight (< 2500 g) births per GP.

In order to look at regional variation in admission into neonatal units we have used two data sources for births. The HES data set allows us to specifically query the number of births per GP for the period of Badger data we are using (2011 and 2012). When using HES data, however, we need to accept a small inconsistency with the number of births as recorded by the ONS. In 2012 there were 16,883 births in HES (extracted using procedure codes R14–R27). This is 93% of the births recorded by the ONS. There is therefore a small discrepancy between HES and ONS. However, in order to confirm any geographical pattern we also used the number of births per year per GP from the NHS England General Practice profiles. This birth rate is predicted from the fertility rate derived from ONS births, but is not specific to the period of Badger data.

We found that admission rates of births into neonatal care varied by geography (Figures 8 and 9). Generally, higher rates for admissions were found in and around Plymouth regardless of the birth data set used.

FIGURE 8.

Admission rates (% births) into neonatal care by GP location (births from HES 2011 and 2012). Each symbol indicates the location of GP practices. Symbol colour indicates the percentage of infants requiring care in a neonatal unit: green, < 10%; yellow, 10–20%; red, 20–30%; purple, > 30%. © 2015 Google – Map data © 2015 Google.

FIGURE 9.

Admission rates (% births) into neonatal care by GP location (births from Public Health England). Each symbol indicates the location of GP practices. Symbol colour indicates the percentage of infants requiring care in a neonatal unit: green, < 10%; yellow, 10–20%; red, 20–30%; purple, > 30%. © 2015 Google – Map data © 2015 Google.

In the Peninsula region, 98.7% of demand where the mother’s primary care trust affiliation was known, or 97.4% of all demand, came from GPs affiliated to the Peninsula region. Of the demand from women with GPs associated with the Peninsula region, 97.8% give birth in one of the five hospitals with a neonatal unit, 1.0% give birth in a local hospital without neonatal care, and 1.1% give birth at home. Of those booked into a main network hospital, 95% are booked into the hospital closest to their GP, 98% deliver in the booked hospital of delivery and 93% deliver in the hospital closest to their home location.

Deprivation scores for the region

In order to better understand the demographics of the Peninsula region, the Index of Multiple Deprivation (IMD) for each Super Output Area was obtained from the ONS. 20 Super Output Areas are on average 2 km across (the average distance between centroids) and contain ≈ 1600 people. They are used by the ONS and are designed to be roughly equal in population across areas. The majority of the Devon and Cornwall region has higher than the national average index of deprivation, with Plymouth especially containing many Super Output Areas in the top national quartile of deprivation (Figure 10).

FIGURE 10.

Map showing deprivation index across the Peninsula region. Colours indicate national quartiles of IMD. IMDs are colour-coded as green, lowest quartile (IMD < 9.8); yellow, second quartile (IMD 9.8–17.2); orange, third quartile (IMD 17.2–30.2); red, highest quartile (IMD 30.2–87.8). © 2015 Google – Map data © 2015 Google.

Incidence of births requiring neonatal care

Although use of neonatal care is associated with low gestational age infants, we found that the majority of infants using neonatal care were of the higher gestational ages. Ninety per cent of those using neonatal care had a gestational age at birth of 32 weeks or higher, and 50% had a gestational age of 38 weeks or higher (Figure 11). Unsurprisingly, there was a strong link between gestation age at birth and birthweight (Figure 12).

FIGURE 11.

Frequency of infants in neonatal care by gestational age at birth. The bars show the proportion of infants in neonatal care categorised by gestational age at birth. The line shows the cumulative contribution.

FIGURE 12.

Relationship between gestation age at birth and birthweight for infants requiring neonatal care.

Although the majority of infants using neonatal care are of the higher gestational ages, the lower gestational ages make a greater contribution to total cot-days used (Figure 13) or total nurse workload (Figure 14). While infants under 34 weeks account for only 12% of all infants who enter into neonatal care, they account for 49% of all cot-days and 61% of nurse workload.

FIGURE 13.

Relative contribution to cot-days by gestational age. The bars show the proportion of cot-days contributed by infants categorised by gestational age at birth. The line shows the cumulative contribution.

FIGURE 14.

Relative contribution to workload by gestational age. The bars show the proportion of workload (nurse equivalents required to care for the infant) contributed by infants categorised by gestational age at birth. The line shows the cumulative contribution.

The South West Peninsula has no specialist surgical and cardiac care unit. Those infants requiring such specialist care must travel outside the network (usually to Bristol). Overall, 2.9% of infants required specialist surgical and cardiac care. The proportion was highly dependent on gestational age at birth (Figure 15). Of those under 30 weeks’ gestation at birth, 16% required surgical or cardiac care.

FIGURE 15.

Proportion of infants in neonatal care who also require care in specialist surgical/cardiac unit, categorised by gestational age at birth.

Contribution of care levels to infants in care and workload

The contribution of each care level to the average number of infants present and the average workload across the network is shown in Figure 16. The higher levels of care, as expected, contribute to workload disproportionately to their numbers. For example, on average 8% of infants in care at any one time are in IC, but those infants account for 24% of the total workload. At the other end of the care spectrum, infants in TC account for 12% of infants in care at any one time, but for only 4% of the total workload.

FIGURE 16.

Contribution of different care levels to the average number of infants and total workload. (a) Average proportion of infants in each category of care present in the network. (b) Relative contribution of workload (nurse requirements) by category of care. IC infants, for example, account for 8% of infants present in neonatal care but 24% of the total nurse workload.

Use of care levels, and lengths of stay, by gestational age at birth

Across all gestational ages, 13% of infants use IC, 23% use HDC, 91% use SC and 61% use TC (Figure 17). Averaging across all gestational ages, the LoS per infant was 9.1 days (0.8 in IC, 1.5 in HDC, 5.7 in SC, 1.2 in TC; Figure 18).

FIGURE 17.

Care requirements for infants accessing neonatal care. The bars show the percentage of infants in neonatal care using each care level. HDU, high-dependency unit; SCU, special care uint.

FIGURE 18.

Average LoS (days) in neonatal care across all infants. Infants not requiring any particular level of care are assigned a zero LoS for that care level when calculating average lengths of stay.

Use of different levels of care and lengths of stay in each level of care vary with gestational age at birth (Figures 19–21). Use of IC drops from 100% at 23 weeks to 70% at 30 weeks and 10% at 34 weeks. HDC use peaks at 92% at 27 weeks and is 90%, 43% and 12% at 30, 34 and 38 weeks. Use of SC is about 90% or higher from 27 weeks.

FIGURE 19.

Use of care levels stratified by gestational age at birth. The bars show the proportion of infants at each gestational age at birth requiring care in each of the four levels of care: (a) IC; (b) HDC; (c) SC; (d) TC.

FIGURE 20.

Average LoS (including zero LoS) in neonatal care stratified by gestational age at birth (note: averages are for all infants of that gestational age; infants not using a particular care level are deemed to have zero LoS in this analysis).

FIGURE 21.

Average LoS (excluding zero LoS) in neonatal care levels stratified by gestational age at birth (note: averages are for infants using a particular care level; infants not using a particular care level are excluded from the calculation).

In order to understand how frequently infants are likely to return to an ICU after transfer to a lower-graded hospital, we examined the number of infants, excluding surgical infants, who have more than one period of stay in IC at the network ICU at Plymouth. These returning infants accounted for 1.3% of all IC episodes at the network ICU.

We assessed how many infants had a contiguous stay in neonatal care by assessing the proportion of infants who had at least 1 day missing in accounted LoS between first admission and last charge (we excluded those infants sent out of network for surgical or cardiac care; some small gaps are expected when transfers occur, as the subsequent admission time will be later than the last recorded discharge time). A total of 98.3% of infants had a contiguous stay in neonatal care. We also found that only 0.3% of infants had a missing period of care of more than 7 days.

A total of 98.6% of infants were admitted into neonatal care within 1 week of their birth, 88.9% were admitted within 2 days of their birth and 81.5% were admitted within a day of their birth.

Gestational equivalent age at discharge

We analysed when infants were discharged home (including foster homes) expressed as gestational age equivalent (Figure 22). The overall average gestational age equivalent was 39.1 weeks. There was a clear relationship between gestational age at admission and gestational age at discharge, with gestational age at discharge first reducing as gestational age at admission increases and then, after about 34 weeks‘ gestational age at admission, increasing as gestational age at admission increases.

FIGURE 22.

Mean gestational age equivalent at discharge to home. Gestational age equivalent is calculated by adding the total LoS before final discharge to the gestational age at birth.

We saw a small but significant (p < 0.001, analysis of variance) effect of the last place of care on gestational age equivalent at discharge to home (Table 6 shows interhospital comparison). There was less than a 1-week difference between the lowest and highest gestational age equivalent at discharge, but we noted that the Royal Devon and Exeter Hospital had a statistically significant greater age at discharge to home than other hospitals.

As all units have SC units (SCUs) and, at least in theory, all infants should have their SC episode in their local hospital, we performed some additional analysis on lengths of stay in SC when infants required only SC and had that SC in just one hospital (Table 7). Exeter had the longest average LoS in SC, at 6.6 days, with Plymouth having the shortest, at 4.0 days.

Discharge destination

Figure 23 shows the discharge destination of the last episode. The final outcome cannot be accurately discerned from these data. For example, at early gestational age, up to 30% may be discharged to another hospital (e.g. a specialist surgical/cardiac unit). It is likely that many of these infants may have died, as they are not recorded as having returned from the other hospital. Likewise, from 34 weeks onwards, approximately 15–25% are recorded as being discharged to another ward in the hospital. It is likely that these are on their way home, but we cannot be sure from these data. However, it is clear that, as expected, mortality rates in neonatal units drop to near zero by 30 weeks of gestational age at birth.

FIGURE 23.

Recorded discharge destination of last episode of care. The lines show the proportion of infants, categorised by gestational age at birth, discharged to home (or foster care), another hospital or another ward (not in the neonatal unit) in the same hospital, or who died.

Of those who were sent to a specialist surgical or cardiac unit, 61% were recorded as going home on the last recorded discharge, 33% went to another hospital, 4% went to a ward outside the neonatal unit and 2% died (Figure 24).

FIGURE 24.

Recorded discharge destination, for last episode of care, of those receiving specialist cardiac or surgical care. The chart shows the proportion of infants discharged to home (or foster care), another hospital or another ward (not in the neonatal unit) in the same hospital, or who died.

Daily variation in case mix, workload and nursing numbers

Each record from Badger lists the entire episode from admission to discharge (see Figure 4). There is no distinction in the sequence of care-level days. When calculating occupancy by care level, we have used a working assumption that, when more than one care level is present in an infant’s spell, then the infant proceeds from higher to lower care levels. Hence, the sequence of stay is surgical → IC → HDC → SC → TC. Although this frequently will be the case, analysis based on this working assumption should be seen as a guide rather than fully definitive. This will not change estimates based on averages over time, or change the daily variation in total infants in care, but may cause small inaccuracies in the daily variation of individual care levels. The simulation model has been tested with the opposite assumption, that infants require increasing care levels (see Chapter 3, Patient flow).

When analysing the occupancy of the units we have used data from March 2011 to December 2012. The first 2 months of data (January and February 2011) provide a ‘run-in’ period, which is required, as calculated occupancy of the unit begins at zero on 1 January 2011 and we track new admissions from that date.

Day-to-day variation in infants in care at different care levels is shown for each hospital in Figure 25.

FIGURE 25.

Daily variation in infants and case mix. The charts show the number of infants (averaged across each day) in ICU (light green), HDU (blue), SCU (dark green) and TC (black). (a) Plymouth; (b) Barnstaple; (c) Truro; and (d) Exeter. HDU, high-dependency unit.

Day-to-day variation in infants, workload and nurses present (audited daily for the morning shift; data excluded staff dedicated to management or to transport services) is shown in Figure 26. Each day the nurse workload was calculated as described in Quantification of workload and lengths of stay in each care level. Where infants were present for only a portion of the day their contribution to workload was weighted accordingly (an infant receiving IC for half a day would contribute 0.5 to workload that day).

FIGURE 26.

Daily variation in infants, workload and nurses present in each hospital. The number of infants and the workload (the nurse equivalents required to care for infants in care) were averaged each day. The number of nurses is the number of nurses (excluding supernumerary nurses) recorded as being present on the day shift. (a) Plymouth; (b) Barnstaple; (c) Truro; and (d) Exeter.

Figures 27 and 28 show the variation in workload relative to average workload in each hospital. Each day the nurse workload was calculated as described in Quantification of workload and lengths of stay in each care level weighted by how much of the day infants were present, and divided by the average workload for the 2-year analysis period.

FIGURE 27.

Distribution of inter-day variation in workload by hospital. Workload is normalised to average workload (values of less than 1 on the x-axis represent days with lower than average workload, and values greater than 1 represent days with higher than average workload). (a) Plymouth; (b) Barnstaple; (c) Truro; (d) Exeter; and (e) Torbay.

FIGURE 28.

Histogram of daily variation in the number of infants present in each hospital. Workload is expressed as a ratio to average number of infants present (values of less than 1 on the x-axis represent days with lower than average workload, and values greater than 1 represent days with higher than average workload). (a) Plymouth; (b) Barnstaple; (c) Truro; (d) Exeter; and (e) Torbay.

Figure 29 shows a frequency plot of nurse workload relative to the number of nurses present. Each day the nurse workload was calculated as described in Quantification of workload and lengths of stay in each care level weighted by how much of the day infants were present. This was divided by the number of nurses present on the day shift for that day. Numbers greater than 1 represent workload exceeding BAPM guidelines.

FIGURE 29.

Variation in ratio of nurses to workload (values of less than 1 represent workload that is within BAPM guidelines for the number of nurses present, values of greater than 1 represent workload in excess of BAPM guidelines).

A statistical summary is shown in Table 10. Table 11 shows the ratio of peak to trough workloads at each hospital. Peak and trough workloads were taken as the 90th and 10th percentile workloads for each hospital. Eighty per cent of days fall between these levels; these levels therefore describe the commonly occurring peaks and troughs, rather than describing very rare variations in workload.

From the data it is apparent that hospitals must cope with very significant variability. The number of IC infants at Plymouth, for example, ranged from zero to nine, while the total number of infants in care ranged from 13 to 35.

We saw significant differences between hospitals in their workloads relative to BAPM standards. Nurse workload at Plymouth, the network ICU, was an average of 127% of BAPM guidelines and 85% of days were spent with workload in excess of BAPM guidelines. All other units worked within BAPM guidelines on average, although they had between 1% (Torbay) and 35% (Truro) of days with workload in excess of BAPM guidelines. At Plymouth, workload peaked at over twice BAPM guidelines whereas at other hospitals it peaked at about 50% above BAPM guidelines (apart from Torbay, which had a peak workload of 9% above BAPM guidelines).

Smaller units had relatively higher variability in the number of infants and workload, the normal range of variation being a greater percentage of average workload. We calculated the typical range of workload as being represented by the 10th percentile and 90th percentile workloads (Table 11 and see Figures 27 and 28 for histograms). The two smallest units had a range of three- to fourfold differences between peak load (90th percentile) and trough load (10th percentile) in both workload and the total number of infants present. The three larger units (Plymouth, Exeter and Truro) had typical ranges of workload and infants of 1.5–2.0 times. Smaller units thus require greater spare capacity to cope with the increased fluctuation in workload (if transfers due to lack of capacity are to be avoided).

Workload using 2011 versus 2001 guidelines from the British Association of Perinatal Medicine

In 2011, BAPM updated its guidelines on what level of care is ascribed to different care interventions. 7 In this report we are using the BAPM 2011 guidelines throughout, but Badger also reports 2001 levels of care, so we undertook a comparison (Table 12). Overall we found that calculated nurse workload (see Chapter 7, Analysis framework in R, for calculation) reduced by 14% between BAPM 2001 guidelines and BAPM 2011 guidelines if TC was excluded from the calculation or 9% if TC was included. Generally, Badger data indicate a reduction in IC days, an increase in HDC days, a reduction in SC days and the categorisation of some as TC (which was missing from the 2001 categorisation).

Software

The model was built using Simul8 2012 Educational Edition (Simul8 Corporation, Boston, MA, USA) (equivalent to Simul8 2012 Professional).

Data used

Data used for the basis of input into the model were obtained from the Badger database of care. The model was built based on data from admissions between 1 January 2011 and 31 December 2012.

Model description

Patient flow

As described in Chapter 7, Daily variation in case mix, workload and nursing numbers, the Badger data we used did not describe the sequence of levels of care within a spell at one hospital. Although the model allows for complex routing we have assumed that the flow proceeds from more to less IC. Hence, the sequence of stay is surgical → IC → HDC → SC → TC (Figure 30). Infants may enter care at any level and exit from any level. Not all care levels between entry and exit points must be used (e.g. an infant may enter IC and transition to SC without using HDC). For modelling we have assumed that all neonatal care occurs continuously after first admission, that is there are no discharge/readmissions (in our data, fewer than 2% of infants had a break in care; see Chapter 7, Use of care levels, and lengths of stay, by gestational age at birth).

FIGURE 30.

Patient flow through model. Note: any one or more levels may be bypassed. An infant could, theoretically, exit IC and enter TC directly.

Table 15 describes the entry, exit and transition proportions. For example, of category 3 infants, 91.8% enter by way of needing IC, with the remainder entering through needing HDC. Of those who need IC, 94.0% proceed to HDC and 6.0% exit the system without use of any more care levels. Infants requiring surgical/cardiac care all enter by way of needing IC in the surgical/cardiac unit (this is a simplification compared with real life). Then 45% of those infants require a second spell of IC, but this may be in a non-specialist unit. Of those entering a second spell of IC, 33.3% move to HDC, 13.3% move directly to SC and the rest exit the system.

TABLE 15 - Entry points and transition between levels of care (proportions of infants)

NA, not applicable.

Infants requiring specialist surgical/cardiac care enter IC in the specialist unit in the model. From the specialist care unit they exit IC in the proportions given by category 7*. Subsequently they follow the transition described by category 7.

Infant category	Entry point					Exit from IC					Exit from HDC			Exit from SC
	IC	HDC	SC	TC	Exit	IC	HDC	SC	TC	Exit	SC	TC	Exit	TC	Exit
1	100.0	0.0	0.0	0.0	0.0	NA	11.1	0.0	0.0	88.9	100.0	0.0	0.0	0.0	100.0
2	100.0	0.0	0.0	0.0	0.0	NA	66.0	0.0	0.0	34.0	90.3	0.0	9.7	42.9	57.1
3	91.8	8.2	0.0	0.0	0.0	NA	94.0	0.0	0.0	6.0	98.6	0.0	1.4	75.0	25.0
4	49.2	35.5	15.3	0.0	0.0	NA	86.6	7.6	0.0	5.9	100.0	0.0	0.0	80.9	19.1
5	8.4	30.5	59.8	1.3	0.0	NA	83.1	16.9	0.0	0.0	99.7	0.3	0.0	80.0	20.0
6	6.1	10.7	73.9	9.3	0.0	NA	43.2	49.4	0.8	6.6	96.2	2.8	0.9	53.9	46.1
7	NA	NA	NA	NA	NA	NA	61.2	13.4	0.0	25.4	90.0	0.0	10.0	39.7	60.3
a7* (surgical/cardiac unit)	100.0	0.0	0.0	0.0	0.0	43.0	12.2	43.6	1.3	0.0

In order to test the effect of the simplification that infants proceed from higher-dependency to lower-dependency levels of care, we ran the model with the reverse assumption (infants enter the system at the lowest level of care that they will receive and exit from the highest level of care that they receive). We found no significant difference in occupancy results in the model (R² > 0.99 for comparison of occupancies of each unit at each care level) or parent travel distances (22.4 km when reversed, compared with 21.8 km when normal), although we did see a 20% reduction in transfers and 10% reduction in transfer distances when the clinical progression order was reversed for all infants. These results therefore suggest that occupancy and parent distance levels are unaffected by clinical progression order, but that the number and distance of transfers may be somewhat affected if normal clinical progression order is not from worse to better. See Appendix 2 for detailed results.

Further support that this generalised flow is a reasonable simplification to make is the observation that only 1.3% of IC episodes at the network ICU at Plymouth are infants returning from a lower-level unit.

A further simplification in the model is that neonatal care is assumed to occur in a contiguous sequence, without readmission after discharge (where there were readmissions in the observed data the total lengths of stay were summed when analysing lengths of stay). We believe this to be a reasonable simplification given that 98.7% of infants in our observed data did have a contiguous period in neonatal care.

Lengths of stay in each care level (where an infant uses that care level) are shown in Table 16. The length of spell in the surgical unit was estimated from the time the infant leaves the Peninsula network for the surgical/cardiac unit until the time they return. The resulting value of 5.8 days was extremely close to the 5.6 days reported in an analysis of the surgical/cardiac unit in Southampton. 28 Apart from category 7 infants (requiring specialist surgical or cardiac care), all lengths of stay were calculated for infants whose full care was available from the network Badger records (these accounted for 96% of all infants not requiring specialist surgical or cardiac care).

TABLE 16 - Lengths of stay with coefficient of variation

CV, coefficient of variation = SD/mean; HDU, high-dependency unit.

Infants requiring specialist surgical/cardiac care enter IC in the specialist unit in the model. From the specialist care unit they exit IC in the proportions given by category 7*. Subsequently they follow the transition described by category 7.

Category of infant (gestational age)	LoS (mean days)				% CV
	ICU	HDU	SCU	TC	ICU	HDU	SCU	TC
1 (< 24 weeks)	4.5	76.7	17.9	0.0	85.9	0.0	0.0	0.0
2 (24 to < 27 weeks or < 28 weeks for twins)	17.3	38.1	29.9	3.1	82.5	52.1	40.0	88.2
3 (27 to < 30 weeks)	10.1	19.0	32.3	3.1	65.3	67.7	38.7	69.6
4 (30 to < 33 weeks)	3.8	6.1	24.1	3.1	89.8	90.9	39.2	66.4
5 (33 to < 36 weeks)	2.6	2.9	9.4	2.4	75.2	126.1	74.0	70.0
6 (≥ 36 weeks)	2.7	2.3	3.2	1.6	100.0	198.8	123.5	78.9
7 (infants requiring specialist surgical/cardiac care), LoS outside surgical/cardiac unit	10.8	16.5	9.7	1.9	116.2	116.9	149.1	114.6
a7* LoS in surgical/cardiac unit	5.80				100

The distribution of lengths of stay at all levels and stratification categories was examined (see Appendix 3 for details). The log-normal distribution offered a high degree of generality across the gestational age and care type, and so this distribution was selected for use for all lengths of stay.

Mortality model

As part of the health economics evaluation, and in order to explore how we might link outcomes to the status of units, we linked mortality to the percentage occupancy of units, based on combining models developed by Tucker et al. ,29 who linked mortality to unit occupancy, and Manktelow et al. ,30 who linked mortality to infant characteristics. The infant characteristics are those found in most predictive models (i.e. gestation, gender and birthweight), which account for most infant mortality in this age group. 31 This is intended to be purely experimental. The equations used are shown in Appendix 4.

There are some challenges to replicating Tucker et al. ’s workload measure. 29 Tucker et al. used three workload measures, and reported that occupancy was statistically significant. Tucker et al. based occupancy on the maximum number of infants present in a unit over their study period but their measure is hard to operationalise in a model. We define occupancy as a proportion of the workload corresponding to 150% BAPM guidelines (i.e. we assume that units are full when at 150% guideline workload). Further, we measure occupancy at the moment of entry of infants to IC, which contrasts with Tucker et al. , who measured average occupancy using twice-daily sampling over the entire period of stay.

In this mortality model, infants are assigned a gestational age and weight according to the distribution found in the Peninsula network (Figure 31) applying an 8% coefficient of variation (CV) reflecting the average CV in the data.

FIGURE 31.

The relationship between gestational age at birth and birthweight used in model (based on Badger data).

In applying the mortality model we add Tucker et al. ’s workload measure to Manktelow et al. ’s 2013 model of infant mortality and make the following additional assumptions:

• Mortality occurs to infants when in IC only (supported by Badger data).

• When infants are discharged directly from IC it will be because of mortality (supported by Badger data).

In the USA it has been found that there is reduced mortality with increased centralisation; this is attributed to increased experience of staff in the more centralised units. 32 At the time of running the analysis, this finding had not been replicated in a UK setting, and it is not modelled here, but recent work has suggested that increased centralisation in the UK can reduce mortality. 33 There may also be risks associated with transfers of infants to IC, but circumstantial evidence suggests that these risks are quite small,33 and we not have modelled them here.

In the simulation model with a mortality component, the transition matrix (entry, transition and exit from levels of care, depending on gestational age category) was based on surviving infants only. Likewise, normal lengths of stay at each care level were based on surviving infants only. All mortality was considered to occur after the IC spell. If an infant was labelled as a mortality then the LoS in IC was set to the average of those infants who had died (this was 5.6 days with a 220% CV). The infant then exits the model after his or her IC stay.

Simulation model validation

In order to validate the model, the output from the model was tested against actual data from 1 January 2013 to 30 June 2013 (a period of data outside that used to build the model). Two methods of determining the location of births were tested:

1. location of births requiring neonatal care based on actual demand by GP

2. demand based on all births; this was achieved by correlating demand (obtained from Badger) with the number of females aged 15–45 years and the fertility rate of those women (see Chapter 7, Comparison of demand with births, for details).

The expected range of results was estimated using Statistical Process Control methods, where control limits are set at ± 3 standard deviations either side of the mean (these limits are expected to contain 99.7% of data occurring within normal common-type variation). 35 As the model was being compared with 6 months of actual data, the 60-year simulation run was divided into 6-month batches and the mean occupancy levels for each 6-month period were calculated. The standard deviations between these batches were then calculated to set the Statistical Process Control limits.

For validation, the closure to new infant levels were set at 200% BAPM guidelines for Plymouth and 150% BAPM guidelines for other units (in line with observed peak workloads, see Chapter 7, Daily variation in case mix, workload and nursing numbers). Resources were set at the average number of nurses present in 2012 (capacity was limited only by nurses).

Demand based on raw Badger data

In this model, the location of demand was based on the number of admissions per GP, derived directly from the Badger data. The average number of infants in each care level and the total workload are shown in Table 18. Total modelled workload and infants in care were 3% higher than actual levels (reflecting the small reduction in workload and infants between training and test periods of data; see previous subsection). The average number of infants in each care level was within ≈ 1 infant for all care levels, with the model generally having an average just higher than the actual (reflecting the lower demand in the test period).

TABLE 18 - Comparison of modelled with actual workload and infants at each care level, based on demand derived directly from Badger admissions

Occupancy measure	Actual January–June 2013	Model	Error (model – actual)
Infants	68.4	70.2	1.9
Workload	22.9	23.6	0.7
IC	5.5	5.8	0.3
HDC	10.6	11.2	0.6
SC	44.3	44.2	–0.1
TC	8.0	9.0	1.1

The average number of infants in each hospital and care level are shown in Figure 32 and Table 19. The chart also shows the expected range of variation in 6-month mean occupancy levels (6 months is chosen because this is equivalent to the test period of data independent of the data used to create the model).

FIGURE 32.

Comparison of modelled with actual unit and care level occupancy levels, based on demand derived directly from Badger admissions. a, Workload = nurse equivalent of infant load = ICU + HDC unit/2 + SCU/4 +TC/8. The points show the predicted and actual average number of infants present in each hospital and care level over a 6-month period. The error bars show the expected range of variation for any 6-month average from the model. The dashed line shows equality between modelled and actual results. Coefficients of determination: infants in each unit/care level, R² = 0.87; Total infants R² = 0.94 and workload R² = 0.97. Model trained with 2011/12 data, tested against 6-month data from 2013.

TABLE 19 - Comparison of modelled with actual unit and care level occupancy levels, based on demand derived directly from Badger admissions

Levels of units: Plymouth, ICU; Exeter, HDC unit+; Torbay, SCU; Truro, HDC unit+; Barnstaple, SCU. See Hospital types for description of unit types.

Occupancy measure	Location of care	Actual January–June 2013	Model	Error (model – actual)	Model SD between 6-month periods	95% confidence limits
Infants	Plymouth Derriford	23.9	28.3	4.4	2.4	0.3
	Royal Devon and Exeter	18.3	15.8	–2.4	1.5	0.3
	Torbay Hospital	6.8	6.7	–0.1	0.8	0.2
	Royal Truro	14.8	14.6	–0.2	1.6	0.3
	Barnstaple	4.6	4.8	0.2	0.7	0.1
Workload	Plymouth Derriford	8.9	10.4	1.4	0.6	0.1
	Royal Devon and Exeter	6.4	5.8	–0.5	0.2	0.1
	Torbay Hospital	1.6	1.6	0.0	0.6	0.0
	Royal Truro	4.8	4.7	–0.1	0.2	0.1
	Barnstaple	1.2	1.1	–0.1	0.3	0.0
IC	Plymouth Derriford	2.8	3.4	0.7	0.6	0.1
	Royal Devon and Exeter	1.5	1.4	–0.1	0.3	0.1
	Torbay Hospital	0.0	0.1	0.0	0.0	0.0
	Royal Truro	1.1	0.8	–0.2	0.2	0.0
	Barnstaple	0.1	0.0	0.0	0.0	0.0
HDC	Plymouth Derriford	5.3	4.5	–0.8	0.8	0.1
	Royal Devon and Exeter	3.4	4.1	0.7	0.8	0.1
	Torbay Hospital	0.2	0.1	–0.1	0.0	0.0
	Royal Truro	1.4	2.4	1.0	0.6	0.1
	Barnstaple	0.3	0.1	–0.2	0.0	0.0
SC	Plymouth Derriford	12.0	16.9	4.9	1.5	0.2
	Royal Devon and Exeter	12.0	8.6	–3.4	0.9	0.2
	Torbay Hospital	5.1	5.4	0.2	0.8	0.2
	Royal Truro	11.4	9.5	–2.0	1.1	0.2
	Barnstaple	3.7	3.9	0.1	0.6	0.1
TC	Plymouth Derriford	3.8	3.4	–0.4	0.3	0.0
	Royal Devon and Exeter	1.4	1.7	0.3	0.2	0.0
	Torbay Hospital	1.4	1.1	–0.2	0.1	0.0
	Royal Truro	0.9	1.9	1.0	0.2	0.0
	Barnstaple	0.5	0.8	0.3	1.0	0.0

Correlations of actual with modelled occupancy data were all high (R² > 0.85 in all comparisons; R² = 0.87 for comparison of infants present at each care level and hospital, R² = 0.94 for total infants present at each hospital, R² = 0.97 for workload at each hospital). These results reflect a generally high degree of fidelity between modelled and actual data.

While the model generally had very good predictive ability, some differences occurred between modelled and actual data. The model predicted 28 infants in care in Plymouth against an actual 24. This difference came mostly from SC infants, where the model predicted an average of 17 in care against an actual of 12. The reduced actual versus modelled results are not due to the model underestimating total infants in SC, as both the model and the actual results have an average of 44 infants in SC (see Table 18). Nor is the difference due to Plymouth discharging infants earlier (see Chapter 7, Gestational equivalent age at discharge). The difference appears to be due to the model placing more infants for SC in Plymouth rather than in Exeter, which shows an underestimate of 3.5 infants in SC, despite Exeter keeping infants in care until a slightly older gestational age equivalence (see Chapter 7, Gestational equivalent age at discharge). From our data we cannot determine why more SC infants are in Exeter and fewer are in Plymouth than the model predicts. Possibilities include (1) parents’ choice and (2) desire to reduce workload in Plymouth, moving infants who are approximately equidistant between Exeter and Plymouth.

Table 20 shows the difference between modelled and actual distances from hospital of care to home (GP) location, and the total number of transfers. The model predicted average distance of parents to home (GP) location within 2 km (within 10%) and the number of transfers very closely, within 2% accuracy. When comparing travel distances we have excluded those transfers in the model to the surgical/cardiac unit in Bristol because we do not have the parallel data in our Badger set. We might know they are transferred out to Bristol, but in the case of a death we do not know how long they spend in Bristol in the real data set. Elsewhere in the model results, all travel distances are included.

TABLE 20 - Comparison of modelled with actual travel distance and transfers, based on demand derived directly from Badger admissions

Distances exclude surgical/cardiac infants out of network.

Parameter	Actual	Model
Transfers/year	389	384
Average distance from home to hospital (km)a	21.3	19.6

Demand based on births per general practitioner

In this model, demand was based on correlating demand from Badger with the number of females aged 15–45 years and the fertility rate of those women. GP Practice Profiles lists the number of women aged 15–45 years for each GP practice, and shows the fertility rate of those women. From these data we calculated births per year per GP. We correlated the number of births per year per GP with the number of neonatal admissions per GP per year from Badger. The demand was then based on the number of births per GP multiplied by the rate of admissions per birth (15.5%, see Chapter 7, Comparison of demand with births, for details). The average number of infants in each care level and the total workload are shown in Table 21. The model, again, showed very good prediction of the total number of infants in each care level, and total infants and workload.

TABLE 21 - Comparison of modelled versus actual workload and infants at each care level, based on demand derived from births per GP

Occupancy measure	Actual January–June 2013	Model	Error (model – actual)
Infants	68.4	69.9	1.6
Workload	22.9	23.6	0.7
IC	5.5	5.9	0.4
HDC	10.6	11.1	0.5
SC	44.3	44.0	–0.3
TC	8.0	8.9	1.0

The average number of infants in each hospital and care level are shown in Figure 33 and Table 22. Correlations of actual with modelled data were all high (R² > 0.90 in all comparisons; R² = 0.91 for comparison of infants present at each care level and hospital, R² = 0.94 for total infants present at each hospital, R² = 0.98 for workload at each hospital).

FIGURE 33.

Comparison of modelled with actual unit and care level occupancy levels, based on demand derived from births per GP. a, Workload = nurse equivalent of infant load = ICU + HDC unit/2 + SCU/4 +TC/8. The points show the predicted and actual average number of infants present in each hospital and care level over a 6-month period. The error bars show the expected range of variation for any 6-month average from the model. The dashed line shows equality between modelled and actual results. Coefficients of determination: infants in each unit/care level, R² = 0.91; Total infants R² = 0.94 and workload R² = 0.98. Model trained with 2011/12 data, tested against 6-month data from 2013.

TABLE 22 - Comparison of modelled with actual unit and care level occupancy levels, based on demand derived from births per GP

Levels of units: Plymouth, ICU; Exeter, HDC unit +; Torbay, SCU; Truro, HDC unit +; Barnstaple, SCU. See Hospital types for description of unit types.

Occupancy measure	Location of care	Actual January–June 2013	Model	Error (model – actual)	Model SD between 6-month periods	95% confidence limits
Infants	Plymouth Derriford	23.9	21.4	–2.4	2.0	0.3
	Royal Devon and Exeter	18.3	16.6	–1.7	1.9	0.3
	Torbay Hospital	6.8	8.5	1.8	1.0	0.2
	Royal Truro	14.8	17.7	2.9	1.6	0.3
	Barnstaple	4.6	5.6	1.0	0.7	0.1
Workload	Plymouth Derriford	8.9	8.3	–0.6	0.8	0.1
	Royal Devon and Exeter	6.4	6.3	–0.1	0.2	0.1
	Torbay Hospital	1.6	2.1	0.4	0.6	0.0
	Royal Truro	4.8	5.6	0.9	0.2	0.1
	Barnstaple	1.2	1.4	0.1	0.3	0.0
IC	Plymouth Derriford	2.8	3.2	0.4	0.7	0.1
	Royal Devon and Exeter	1.5	1.6	0.1	0.3	0.0
	Torbay Hospital	0.0	0.1	0.0	0.0	0.0
	Royal Truro	1.1	1.0	–0.1	0.2	0.0
	Barnstaple	0.1	0.1	0.0	0.0	0.0
HDC	Plymouth Derriford	5.3	3.4	–1.9	0.6	0.1
	Royal Devon and Exeter	3.4	4.6	1.2	0.9	0.2
	Torbay Hospital	0.2	0.1	–0.1	0.0	0.0
	Royal Truro	1.4	2.9	1.4	0.7	0.1
	Barnstaple	0.3	0.1	–0.2	0.0	0.0
SC	Plymouth Derriford	12.0	12.4	0.4	1.2	0.2
	Royal Devon and Exeter	12.0	8.7	–3.3	1.1	0.2
	Torbay Hospital	5.1	6.9	1.7	0.9	0.1
	Royal Truro	11.4	11.5	0.1	1.0	0.2
	Barnstaple	3.7	4.5	0.8	0.6	0.1
TC	Plymouth Derriford	3.8	2.5	–1.3	0.2	0.0
	Royal Devon and Exeter	1.4	1.7	0.3	0.2	0.0
	Torbay Hospital	1.4	1.4	0.1	0.2	0.0
	Royal Truro	0.9	2.3	1.4	0.2	0.0
	Barnstaple	0.5	0.9	0.5	1.0	0.0

Basing demand on births per GP produced correlations between actual and modelled occupancies of units and care levels that were as good as basing demand on admissions into neonatal care from each GP. The model produces some different results from basing demand on neonatal admissions. While overall correlations again are excellent, we see some individual results where we can spot some differences between modelled and actual results. The number of infants in Plymouth is now slightly underestimated (by an average of 2.4 infants or 10%) whereas the number of infants in Truro is slightly overestimated (by an average of 2.9 infants or 20%). It should be noted that these differences are within the expected range of variation between 6-month periods, but they may reflect the fact that demand does not match exactly to births (see Chapter 7, Comparison of demand with births, for more details).

Average distance of parents from the point of care to home (GP) location in this model is very close to actual results (21.2 km vs. 21.3 km, Table 23). The number of transfers (446 modelled vs. 389 actual) is higher in the model by about 15%.

TABLE 23 - Comparison of modelled with actual travel distances and transfers, based on demand derived from births per GP

Distances exclude surgical/cardiac infants out of network.

Parameter	Actual	Model
Transfers/year	389	446
Average distance from home to hospital (km)a	21.3	21.2

Peninsula network: scenario results

Localisation versus centralisation: all levels of care

A key aim of the model was to evaluate the effect of altering the levels of localisation or centralisation on both units and parents. Eight example scenarios were used to assess this effect (Table 24). The scenarios are:

TABLE 24 - Scenarios used to study the effect of centralisation of service provision

Hospital	Scenario
	1	2	3	4	5	6	7	8
Plymouth Derriford	ICU	ICU	ICU	ICU	ICU	ICU	ICU	ICU
Royal Devon and Exeter	ICU	LNU+	LNU	SCU	LNU	LNU	Births only	Closed
Torbay Hospital	ICU	SCU	SCU	SCU	Births only	Closed	Births only	Closed
Royal Truro	ICU	LNU+	LNU	SCU	LNU	LNU	Births only	Closed
Northern Devon (Barnstaple)	ICU	SCU	SCU	SCU	Births only	Closed	Births only	Closed
St Michael’s Hospital, Bristol	Surgical	Surgical	Surgical	Surgical	Surgical	Surgical	Surgical	Surgical

1. All Peninsula network units offer IC (surgical/cardiac support is offered in Bristol for all scenarios).

2. Plymouth is the network ICU. Exeter and Truro are classified as ‘LNU+’ ‘LNU+’ (see Hospital types for details of unit types) and Torbay and Barnstaple are SCUs.

3. As scenario 2 but Exeter and Torbay are classified as LNUs according to the NHS standard contract for neonatal critical care. 8

4. Plymouth is maintained as the network ICU and all other units are designated SCUs.

5. Plymouth is maintained as the network ICU; Exeter and Truro are classified as LNUs; Torbay and Barnstaple are converted to birthing centres but without any specialised neonatal care (all infants requiring SC or higher require transfer to another unit).

6. As scenario 5 but Torbay and Barnstaple are closed entirely (no births).

7. Plymouth is maintained as the network ICU. All other hospitals are birthing units only.

8. Plymouth is maintained as the sole birthing unit and network ICU. All other units are closed to births and neonatal care.

These scenarios were based on demand from Badger admissions (see Demand based on raw Badger data). The number of nurses required was derived by calculating the 80th, 90th and 95th percentiles of the distribution of workload at each hospital within the Peninsula network. In these scenarios, workload at units is not limited by capacity; these scenarios represent workload and travel distances if all infants are cared for according to planned location depending on unit capabilities.

Throughout all scenarios the number of infants and the total workload in the Peninsula region remained constant (Table 25 and Figure 34). However, the number of nurses required to meet BAPM standards for any given proportion of time fell as centralisation was increased. We saw a maximum 20% reduction in required nursing between the most localised (ICUs in all hospitals) and most centralised (Plymouth was the only birthing and neonatal unit). It should be noted, however, that this reduction was between extremes which are highly unlikely to be considered.

TABLE 25 - Effect of localisation or centralisation on network performance

Measure	Scenario (increasing centralisation)
	1	2	3	4	5	6	7	8
Hospital type
Plymouth Derriford	ICU	ICU	ICU	ICU	ICU	ICU	ICU	ICU
Royal Devon and Exeter	ICU	LNU+	LNU	SCU	LNU	LNU	Births only	Closed
Torbay Hospital	ICU	SCU	SCU	SCU	Births only	Closed	Births only	Closed
Royal Truro	ICU	LNU+	LNU	SCU	LNU	LNU	Births only	Closed
Northern Devon (Barnstaple)	ICU	SCU	SCU	SCU	Births only	Closed	Births only	Closed
St Michael’s Hospital, Bristol	Surgical	Surgical	Surgical	Surgical	Surgical	Surgical	Surgical	Surgical
Average workload
Plymouth Derriford	9.0	10.4	12.4	15.6	12.6	12.7	22.5	23.5
Royal Devon and Exeter	4.5	5.8	4.5	2.5	6.7	7.2	0.3	0.0
Torbay Hospital	2.9	1.6	1.6	1.6	0.2	0.0	0.2	0.0
Royal Truro	5.1	4.7	3.9	2.7	3.9	3.9	0.3	0.0
Northern Devon (Barnstaple)	2.0	1.1	1.1	1.1	0.1	0.0	0.1	0.0
Outside network	1.4	1.4	1.4	1.3	1.3	1.1	1.3	1.2
Average infants present
Plymouth Derriford	26.7	28.1	30.3	36.8	31.4	31.5	67.3	69.9
Royal Devon and Exeter	13.4	15.7	14.5	10.2	23.9	25.3	0.7	0.0
Torbay Hospital	8.5	6.7	7.0	6.7	0.5	0.0	0.5	0.0
Royal Truro	15.1	14.7	13.9	11.4	13.7	13.7	0.8	0.0
Northern Devon (Barnstaple)	6.1	4.8	4.8	4.7	0.3	0.0	0.3	0.0
Outside network	1.7	1.6	1.6	1.6	1.6	1.2	1.6	1.5
General network statistics
Network infants	69.8	70.0	70.2	69.8	69.8	70.5	69.6	69.9
Network workload	23.5	23.6	23.5	23.5	23.5	23.8	23.4	23.5
Network on-duty staff to meet BAPM standards 80% of time	30.0	29.6	29.4	28.6	28.6	27.5	27.0	25.5
Network on-duty staff to meet BAPM standards 90% of time	33.9	33.0	32.6	31.8	31.8	30.3	30.3	27.3
Network on-duty staff to meet BAPM standards 95% of time	37.6	36.3	35.8	34.5	34.8	32.5	32.9	28.5
Staff utilisation to meet BAPM standards 90% of time	69%	72%	72%	74%	74%	79%	77%	86%
Distances and transfers
Mean distance from patient node to point of care (km)	18.9	21.7	23.1	27.6	29.1	29.5	54.3	56.3
% infants over 50 km from parents’ home	3	6	9	16	15	15	55	58
In-utero transfers/year	10	26	36	53	98	26	231	10
In-utero transfer (1000 km/year)	3.6	5.7	7.5	10.5	14.6	6.2	39.0	3.7
Total neonatal transfers/year	130	352	509	711	771	350	1384	129
Total neonatal transfer distance (1000 km/year)	48.5	75.0	103.0	139.4	132.6	82.7	250.0	49.9

FIGURE 34.

Effect of centralisation on network workload and nursing requirements. The graph shows the average workload (the number of nurses required to care for infants in care, black triangles), and 80th (green square), 90th (blue triangle) and 95th percentile (light green circle) workload. The light green circles, for example, show the number of nurses required to meet BAPM standards (without neonatal transfers) 95% of the time.

An issue that the model helps to expose, and to quantify, is the problem of trying to dissociate neonatal planning from maternity planning. Where it is possible to increase efficiency by pooling neonatal care, the benefits are attenuated if neonatal nursing expertise must be retained when the hospitals with closed neonatal units retain birthing facilities. For example, if nursing staff levels are set to achieve BAPM standards 90% of the time, then a theoretical 7% reduction in nursing may be achieved if Torbay and Barnstaple, the two smallest units, are closed, but if neonatal staff must remain to care for infants prior to transfer this saving is reduced to 2.5% unless the temporary neonatal care may be absorbed by the maternity unit.

While the model suggests that savings in nursing staff and cots are possible by consolidating units, it also helps to inform the expected effect this will have on parents and transport services (Table 25 and Figure 35). With the most localised configuration (all hospitals have IC) the average distance from the parents’ home location to the point of care is 19 km (road distance), with 97% being within 50 km (roughly equivalent to a 1-hour journey, see Chapter 6, Road distance and time versus straight-line distances). The configuration approximating current set-up has an average distance of 22 km from the parents’ home location to the point of care, with 94% being within 50 km. If Exeter and Truro are converted to LNUs according to the NHS standard contract for neonatal critical care8 then the model predicts an average distance from the parents’ home location to the point of care of 23 km, with 91% being within 50 km. Closure of the two smallest units (Barnstaple and Torbay) results in an average distance of 29 km from the parents’ home location to the point of care, with 85% being within 50 km. Extreme centralisation, with all neonatal care at Plymouth, would be expected to result in an average distance of ≈ 55 km from the parents’ home location to the point of care, with 42–45% being within 50 km.

FIGURE 35.

Effect of centralisation on distance from parents’ home to location of care. The black circles show the average distance from the point of care to the parents’ home, and the green squares show the percentage of infants cared for more than 50 km away from the parents’ home.

The degree of centralisation also has a very significant effect on the number and distances of transfers in the model (Figure 36 and Table 25). With the most localised scenario (all hospitals have IC) the only transfers required are to and from the surgical/cardiac unit. This amounts to ≈ 130 transfers covering ≈ 50,000 km per year. In this model we assume all transfers require independent to/from journeys; in the real world there may be some efficiencies by carrying infants both ways in the same ambulance, that is taking one infant and collecting another. This compares with the approximately current-state model of ≈ 350 transfers covering ≈ 75,000 km per year. If Exeter and Truro are converted to LNUs according to the NHS standard contract for neonatal critical care8 then the model predicts that transfers increase to ≈ 500 covering ≈ 105,000 km per year. At the most extreme form of centralisation (neonatal care offered only in Plymouth, although births continue at local hospitals) there are potentially ≈ 1400 transfers covering ≈ 250,000 km per year. Clearly any potential gain in nursing and cot-use efficiency must be weighed against increased numbers and distances of transfers as well as the impact on parents.

FIGURE 36.

Effect of centralisation on number and total distance of transfers. The black circles show the expected neonatal transfers per year, and the green squares the total travel distance (assuming a return journey between the two transfer hospitals).

We again see a significant effect of dissociating neonatal care from births. For example in scenarios where two SCUs (Torbay and Barnstaple) are closed (see scenarios 5 and 6, Table 25), the number and distance of transfers are ≈ 770 transfers covering ≈ 130,000 km per year if the units are kept open as birthing units and ≈ 350 transfers covering ≈ 80,000 km per year if the units are closed for both births and neonatal care. If neonatal units are closed while the number of birthing units remains the same, then significant increases in demand on transportation services would be expected.

Localisation versus centralisation: effect on intensive care and high-dependency care

The previous section described modelling around all levels of care. All hospitals offer SC to infants; the specialisation and centralisation are performed at HDC and IC levels. Therefore, in this section we examine, in more detail, the effect of centralisation on IC and HDC only. The workload described is workload just for network IC and HDC levels of care and the travel distances refer only to infants in IC and HDC levels of care. Infants in the surgical unit outside the regional network are also excluded from this analysis. In these models, hospitals are not limited by capacity; all infants are cared for in the planned location.

In this analysis we look at five scenarios (Table 26). To simplify the analysis we assume a zero transfer time from the units not supporting either IC or HDC. The five scenarios are:

1. All five network hospitals are network ICUs (see Hospital types for description of unit types).

2. Plymouth, Truro and Exeter are network ICUs. Torbay and Barnstaple offer no IC or HDC.

3. Plymouth is the network ICU. Exeter and Truro are classified as LNU+ (this approximates current state).

4. As scenario 2 but Exeter and Torbay are classified as LNUs according to the NHS standard contract for neonatal critical care. 8

5. Intensive care and HDC offered in Plymouth only.

TABLE 26 - Scenarios used to study the effect of centralisation on IC and HDC service provision

Location of care	Scenario
	1	2	3	4	5
Plymouth Derriford	ICU	ICU	ICU	ICU	ICU
Royal Devon and Exeter	ICU	ICU	LNU+	LNU	–
Torbay Hospital	ICU	–	–	–	–
Royal Truro	ICU	ICU	LNU+	LNU	–
Northern Devon (Barnstaple)	ICU	–	–	–	–

Centralisation, in the studied scenarios, does not affect total network workload, infants or the number of infants in IC or HDC. The average number of infants in IC or HDC is 17. If infants are to be cared for in the planned location (no transfers due to lack of local capacity) then we see that centralisation does reduce the total number of IC/HDC-capable cots required (Figure 37 and Table 27). With full localisation, 29 cots are required, which is the equivalent of IC and HDC having to run at 58% cot utilisation to avoid unplanned transfers for 90% of IC and HDC infants. Having IC and HDC at only three hospitals (as currently) reduces the required cots to ≈ 27, or 63% target utilisation to avoid unplanned transfers. Interestingly, the designation of Exeter and Truro as ICU, LNU+ or LNU (see Hospital types for description of unit types) has little effect on this number. If all IC and HDC is centralised in Plymouth then the number of cots required is 23, equivalent to IC and HDC having to run at 73% cot utilisation to avoid unplanned transfers for 90% of IC and HDC infants. This latter number of 23 IC/HDC cots is also the total number of IC and HDC cots required to cope with network IC and HDC workload 90% of the time if transfers are allowed to smooth demand across the network. It should be noted that this cot utilisation is significantly below the 85% bed utilisation suggested by Bagust et al. 5 for a 200-bed hospital; this difference is due to the greater variability in workload experienced when average numbers of patients being cared for are lower (as previously discussed in Localisation versus centralisation: all levels of care).

FIGURE 37.

Effect of centralisation on the number of combined IC and HDC cots required to meet demand without unplanned transfers. HDU, HDC unit.

TABLE 27 - Effect of localisation or centralisation on network IC and HDC performance

HDU, high-dependency unit.

Measure	Scenario
	1	2	3	4	5
Hospital type
Plymouth Derriford	ICU	ICU	ICU	ICU	ICU
Royal Devon and Exeter	ICU	ICU	LNU+	LNU	–
Torbay Hospital	ICU	–	–	–	–
Royal Truro	ICU	ICU	LNU+	LNU	–
Northern Devon (Barnstaple)	ICU	–	–	–	–
Workload (mean)
Plymouth Derriford	4.3	4.5	5.5	7.1	11.3
Royal Devon and Exeter	2.2	4.3	3.7	2.7	0.0
Torbay Hospital	1.4	0.0	0.0	0.0	0.0
Royal Truro	2.5	2.5	2.1	1.6	0.0
Northern Devon (Barnstaple)	1.0	0.0	0.0	0.0	0.0
Workload (90th percentile)
Plymouth Derriford	7.0	7.0	9.0	11.0	16.0
Royal Devon and Exeter	4.0	7.0	6.0	5.0	0.0
Torbay Hospital	3.0	0.0	0.0	0.0	0.0
Royal Truro	4.5	5.0	4.0	3.0	0.0
Northern Devon (Barnstaple)	2.0	0.0	0.0	0.0	0.0
Infants (mean)
Plymouth Derriford	6.3	6.7	7.7	9.4	16.8
Royal Devon and Exeter	3.3	6.5	5.8	4.8	0.0
Torbay Hospital	2.1	0.0	0.0	0.0	0.0
Royal Truro	3.7	3.7	3.3	2.8	0.0
Northern Devon (Barnstaple)	1.4	0.0	0.0	0.0	0.0
Infants (90th percentile)
Plymouth Derriford	10.0	10.0	12.0	14.0	23.0
Royal Devon and Exeter	6.0	10.0	9.0	8.0	0.0
Torbay Hospital	4.0	0.0	0.0	0.0	0.0
Royal Truro	6.0	6.0	6.0	5.0	0.0
Northern Devon (Barnstaple)	3.0	0.0	0.0	0.0	0.0
IC infants (mean)
Plymouth Derriford	2.2	2.3	3.4	4.8	5.8
Royal Devon and Exeter	1.1	2.2	1.6	0.7	0.0
Torbay Hospital	0.7	0.0	0.0	0.0	0.0
Royal Truro	1.3	1.3	0.9	0.4	0.0
Northern Devon (Barnstaple)	0.5	0.0	0.0	0.0	0.0
IC infants (90th percentile)
Plymouth Derriford	4.0	5.0	6.0	8.0	9.0
Royal Devon and Exeter	3.0	4.0	3.0	2.0	0.0
Torbay Hospital	2.0	0.0	0.0	0.0	0.0
Royal Truro	3.0	3.0	2.0	1.0	0.0
Northern Devon (Barnstaple)	1.0	0.0	0.0	0.0	0.0
HDC infants (mean)
Plymouth Derriford	4.1	4.4	4.4	4.5	11.0
Royal Devon and Exeter	2.2	4.3	4.3	4.1	0.0
Torbay Hospital	1.3	0.0	0.0	0.0	0.0
Royal Truro	2.4	2.4	2.4	2.4	0.0
Northern Devon (Barnstaple)	1.0	0.0	0.0	0.0	0.0
HDC infants (90th percentile)
Plymouth Derriford	7.0	7.0	7.0	7.0	16.0
Royal Devon and Exeter	4.0	7.0	7.0	7.0	0.0
Torbay Hospital	3.0	0.0	0.0	0.0	0.0
Royal Truro	4.0	4.0	4.0	5.0	0.0
Northern Devon (Barnstaple)	2.0	0.0	0.0	0.0	0.0
General network statistics
Network infants (mean)	16.8	16.8	16.8	17.0	16.8
Network ICU infants (mean)	5.8	5.8	5.8	5.9	5.8
Network HDU infants (mean)	11.1	11.1	11.1	11.1	11.0
Network ICU cots needed to meet workload 90% of time (without unplanned transfers)	13.0	12.0	11.0	11.0	9.0
Network HDU cots needed to meet workload 90% of time (without unplanned transfers)	20.0	18.0	18.0	19.0	16.0
Network ICU/HDU cots needed to meet workload 90% of time (without unplanned transfers)	29.0	26.0	27.0	27.0	23.0
Network workload (mean)	11.3	11.3	11.3	11.4	11.3
Network on-duty staff to meet BAPM standards 90% of time	20.5	19.0	19.0	19.0	16.0
Distances
Mean distance from patient node to point of care (km)	16.1	24.4	27.5	32.5	54.1
% infants over 50 km from parents’ home	1	10	15	23	58

In the above results, it is assumed there is flexibility in cot use between IC and HDC. Without this flexibility the cot requirements increase. For example, in scenario 1 (fully localised), 33 cots (13 IC, 20 HDC) are required if infants must be cared for in distinct IC cots. This compares with 29 cots if cots may be used flexibly. In scenario 5 (fully centralised) 25 cots (9 IC, 16 HDC) are required if infants must be cared for in distinct IC and HDC cots. This compares with 23 cots if cots may be used flexibly.

Centralisation also affects the number of IC and HDC staff required to care for infants, if unplanned transfers are to be avoided (Figure 38, Table 27) and units do not co-opt staff primarily working in SC. The average IC and HDC network workload was 11.3 nurse equivalents. With full localisation, 21 nurses are required at any time, which is the equivalent to IC and HDC having to run at 55% nurse utilisation to meet BAPM standards and/or avoid unplanned transfers for 90% of IC and HDC infants. Having IC and HDC at only three hospitals (as currently) reduces the required nurses to 19, or 60% of target utilisation, to meet BAPM standards and/or avoid unplanned transfers 90% of the time. Again, the designation of Exeter and Truro as ICU, LNU+ or LNU has little effect on this number. If all IC and HDC is centralised in Plymouth then the number of nurses required is 16, equivalent to IC and HDC having to run at 70% of nurse utilisation to meet BAPM standards and/or avoid unplanned transfers 90% of the time. This is also the total number of nurses required to cope with network IC and HDC workload 90% of the time if transfers are allowed to smooth demand across the network. These figures assume an isolated pool of IC and HDC nurses, with no flexibility of work between IC, HDC and SC. Where flexibility of work with SC is possible, the fuller system results in Localisation versus centralisation: all levels of care are more relevant.

FIGURE 38.

Effect of centralisation on the number of on-duty nursing staff needed to support IC and HDC.

As with the full network simulation, centralisation had a significant effect on travel distances for parents with infants in IC or HDC (Figure 39). With IC provided locally, the average distance from the parents’ home location and the location of care is 16 km (road distance), with 99% cared for within 50 km of the home location. With three ICUs, the average distance from the parents’ home location and the location of care is 24 km (road distance), with 90% cared for within 50 km of the home location. In contrast to results on cot and nurse requirements, the type of designation of Exeter and Truro did affect travel distances. If these two hospitals are designated as LNU+ (approximates current configuration) then the average distance is 28 km, with 85% within 50 km. If these units become LNUs then the average distance is 33 km, with 77% within 50 km. If all IC and HDC is centralised in Plymouth then the average distance is 54 km, with 42% within 50 km.

FIGURE 39.

Effect of centralisation on distance from parents’ home to location of care (IC/HDC infants only). The black circles show the average distance from the point of care to the parents’ home, and the green squares show the percentage of infants cared for more than 50 km away from the parents’ home.

Variability in workload, the effect of altering the threshold of closure to new admissions and the effect of altering staffing levels

As observed in Chapter 7, Daily variation in case mix, workload and nursing numbers, there is considerable variability in workloads, with 21–48% CV in workload and 17%–45% CV in the number of infants present. We compared variability in our model with the observed variability (Figure 40, Table 28).

FIGURE 40.

A 2-year run-chart comparison of the number of infants present in each hospital. Light green, IC; dark blue, HDC; dark green, SC; black, TC; light blue, total.

TABLE 28 - Actual and modelled variation in workload and infants present

CV, SD/mean; SD, standard deviation.

Measure	Actual (2011/12)			Model: resource-constrained			Model: resource-unconstrained
	Mean	SD	CV (%)	Mean	SD	CV (%)	Mean	SD	CV (%)
Workload
Plymouth	9.1	1.9	21	10.4	2.5	24	10.3	2.5	24
Exeter	6.7	1.5	22	5.8	1.8	31	5.8	1.8	31
Torbay	1.6	0.7	43	1.6	0.7	43	1.6	0.7	43
Truro	5.5	1.3	24	4.7	1.5	32	4.7	1.5	33
Barnstaple	1.3	0.6	48	1.1	0.6	50	1.2	0.6	51
Infants
Plymouth	24.5	4.1	17	28.3	5.5	20	28.1	5.6	20
Exeter	20.5	3.8	19	15.8	4.2	26	15.9	4.2	27
Torbay	6.5	2.6	40	6.7	2.7	40	6.7	2.6	40
Truro	15.5	3.5	22	14.6	4.0	27	14.8	4.1	28
Barnstaple	4.9	2.2	45	4.8	2.2	46	4.8	2.2	47

We ran the model with capacity constrained by resource (as in the validation model, Demand based on raw Badger data, Plymouth working at up to 200% BAPM guidelines, and other hospitals working at up to 150% BAPM guidelines) and in a model where resource was unlimited. Interday variation was calculated from a once-daily audit count of the number of infants present in either the observed or the modelled data. In the model we see similar, if slightly higher, variability to the observed data; in the resource-constrained model there was 24–50% CV in workload and 20–46% CV in the number of infants present, and in the resource-unconstrained model there was 24–51% CV in workload and 20–47% CV in the number of infants present. As in the observed data, the model predicts higher variability of workload in the smaller units.

Effect of altering threshold of closure to new admissions

We went on to examine how this variability of workload potentially affects the number of transfers, and the distance that infants are from their parents’ home. Unit designations were as scenario 2, Table 25. The resources in the model were set to be the average workload for each hospital in the unconstrained model (i.e. each hospital has on average sufficient resource to cope with average predicted workload if there are no unplanned transfers). The threshold for closure to new admissions was then set at 100–200% BAPM guidelines.

When units may work at up to 200% BAPM workload, and when resources are set at average workload, the average travel distance from the location of care is 22 km (road distance), with 94% of homes within 50 km. As thresholds for closure to new admissions are brought down, average travel distances and the proportion of infants over 50 km from their parents’ home increase (Figure 41). When units close to new infants at 100% BAPM workload, with resources sufficient for average workload, the average travel distance from the location of care is 34 km (road distance), with 81% of homes within 50 km.

FIGURE 41.

Effect of altering closure threshold on distance of infants from parents’ home. The black circles show the average distance from the point of care to the parents’ home, and the green squares show the percentage of infants cared for more than 50 km away from the parents’ home.

When units may work up to 200% BAPM workload, and when resources are set at average workload, there are ≈ 350 transfers per year, covering ≈ 75,000 km. As thresholds for closure to new admission are brought down, the number and total distance of transfers increase (Figure 42). When units close to new infants at 100% BAPM workload, with resources sufficient for average workload, there are ≈ 2400 transfers per year, covering ≈ 500,000 km.

FIGURE 42.

Effect of altering closure threshold on number and distances of neonatal transfers. The black circles show the expected neonatal transfers per year, and the green squares the total travel distance (assuming a return journey between the two transfer hospitals).

Using networks allows work to move to where there is spare capacity. This enables the network to run within guidelines without having to have enough spare resource in every unit to cope with local peaks in demand. However, as this flexibility is used more, the number and distances of transfers increase, and infants become located further from their parents’ home.

Effect of altering staffing to workload ratios

An alternative method of investigating the relationship between BAPM workload and system performance is to maintain a fixed BAPM workload at which the unit closes to new admissions and then adjusts resource levels so that the units are working, on average, at different workload levels relative to BAPM guidelines. Average workload is constant in these scenarios, but the number of nurses are changed so that average capacity utilisation (measured against staffing levels) varies.

In these models, units are closed to new admissions at 150% BAPM guideline workload levels. This level was chosen as being the typical maximum workload level we saw across the hospitals (see Chapter 7, Daily variation in case mix, workload and nursing numbers, for more details). Workload is then expressed as either percentage of BAPM guideline (with values of greater than 100% representing workloads in excess of BAPM guidelines given the nursing resources present) or percentage of maximum permitted workload (with 100% representing the maximum workload permitted before closure to new admissions, equivalent to 150% BAPM guidelines).

When average workload is low (50% BAPM guidelines, 33% maximum permitted workload) the average distance parents are from the location of care is 22 km, with 94% being within 50 km. As resource levels are reduced, increasing the workload relative to BAPM guidelines, the distance parents are from the location of care increases, with significant rises occurring above 70% maximum permitted workload (Figure 43). As average workload approaches the maximum permitted workload (150% BAPM recommendations), the average distance parents are from the location of care rises to 34 km, with 81% being within 50 km.

FIGURE 43.

The relationship between travel distance for parents and unit workload (workload expressed as percentage of maximum permitted workload before closure to new admissions when workload exceeds 150% BAPM guidelines). The black circles show the average distance from the point of care to the parents’ home, and the green squares show the percentage of infants cared for more than 50 km away from the parents’ home.

Average workload levels have a profound effect on the number and total distance of transfers (Figure 44). When average workload is low (50% BAPM guidelines, 33% maximum permitted workload) there are ≈ 350 transfers per year, covering ≈ 75,000 km. Transfers begin to increase significantly above 70% maximum permitted workload. As average workload approaches the maximum permitted workload (150% BAPM recommendations), the number of transfers reaches ≈ 2500 per year, covering 500,000 km.

FIGURE 44.

The relationship between transfers and unit workload (workload expressed as percentage of maximum permitted workload, which is 150% BAPM guidelines). The black circles show the expected neonatal transfers per year, and the green squares the total travel distance (assuming a return journey between the two transfer hospitals).

Mortality modelling

As described in Mortality model, we have sought to investigate incorporating outcome measures into this operational model, and have used published models of mortality to link mortality in the model to (1) infant characteristics and (2) percentage unit occupancy. In these models we have used a workload level of 150% BAPM guidelines workload levels as equivalent to maximum occupancy. This level was chosen as being the typical maximum workload level we saw across the hospitals (see Chapter 7, Daily variation in case mix, workload and nursing numbers, for more details).

None of the model results elsewhere in this report use this mortality predictor unless specified. In the validation model (see Demand based on raw Badger data), when the mortality predictor was used, the model predicted mortality in the region of 1.03%. This compared with an actual mortality (January 2011 to December 2012) of 1.02%.

It must be reinforced, however, that these results are experimental.

We used this model to predict the impact of altering staffing levels in the network. For this experiment there were no closures of units to new admissions; workload was allowed to increase as staffing was reduced. When workload is expressed as a percentage of maximum workload (150% BAPM guidelines), mortality increases from 0.86% at 33% maximum workload to 1.21% at 100% maximum workload (Figure 45).

FIGURE 45.

Effect of altering staffing to workload ratio on predicted mortality (actual workload remains constant, but staffing is altered so workload relative to BAPM guidelines varies). Maximum permitted workload occurs when workload is 150% of BAPM guideline workload for the number of nurses present.

Extension to Western network

By basing demand on births (see Chapter 7, Comparison of demand with births) we modelled the expected performance of the combined Peninsula/Western network. For these scenarios we assumed that use of neonatal care and lengths of stay in the wider region were the same as the Peninsula region. We again looked at the expected impact of progressive centralisation, but this time in the larger network (Table 30). (See Table 14 for description of unit types.) These scenarios generally change all units to different levels at the same time, with the following exceptions:

• One surgical hospital is always maintained.

• One option (scenario 2) allows for two surgical units, with the second unit being the current network ICU in the Peninsula network (Plymouth).

• One option (scenario 5) has a mix of LNU and SCU; the allocation of hospitals is based on current differentials.

With increasing centralisation, we again observe that the number of nurses required to meet BAPM guidelines 90% of the time reduces, with a maximum 21% reduction between the extremes of localisation and centralisation (Figure 46). At the same time, average travel distances for parents increase from 15 km to 94 km with the number of parents over 50 km from the hospital increasing from 1% to 66% (Figure 47). Transfers increase from none to 4700 per year covering ≈ 1 million km per year (Figure 48).

FIGURE 46.

Effect of centralisation on network workload and nursing requirements. The graph shows the average workload (the number of nurses required to care for infants in care, black triangles), and 80th (dark green square), 90th (blue triangle) and 95th percentile (light green circle) workload. The light green circles, for example, show the number of nurses required to meet BAPM standards (without neonatal transfers) 95% of the time.

FIGURE 47.

Effect of centralisation on distance from parents’ home to location of care. The black triangles show the average distance from the point of care to the parents’ home, and the green squares show the percentage of infants cared for more than 50 km away from the parents’ home.

FIGURE 48.

Effect of centralisation on number and total distance of transfers. The black triangles show the expected neonatal transfers per year, and the green squares the total travel distance (assuming a return journey between the two transfer hospitals).

National modelling (England)

Effect of changing the number of units on distance from home location to point of care

Methods

In some pilot work, we have used a simple algorithm to look at the impact of changing the number of neonatal units on the distance from the mother’s home location to the nearest neonatal unit. Location and numbers of infants is based on the number of births for each GP in England.

In this type of modelling, each solution is scored according to a weighted function based on the average distance to a unit, the percentage of parents within a given target distance and the maximum distance any parent has to travel (Table 32). The algorithm places the first unit (from a list of all current units) where the highest score is achieved. The first unit is then ‘locked’ in place and the algorithm repeated to find the second unit that increases the score by the most. The process repeats through all the locations available.

TABLE 32 - Scoring used in location algorithm (target distance 16 km)

Note

Three parameters (average distance from home to location of care, percentage within target distance, and maximum distance from home to location of care) are scored 0–1 along the scale outlined above (scores outside the given range are scored 0 or 1). Scores are then multiplied by the weighting factor for each parameter, and are then summed. This analysis may be applied to either total units or units of a particular type (e.g. how many ICUs are needed to make travel distance acceptable to parents).

Variable	Scoring		Weight
	0	1
Average distance (km)	200	0	50
% within target distance	0	100	50
Maximum distance travelled	500	0	10

This analysis should not be seen in isolation. There may be other reasons for having more or fewer units than simply considering travel distances and there may be other reasons for choosing the location of units other than travel distances. This analysis uses straight-line distance; a fuller model would include estimated road distance and travel times.

Results

There are clearly diminishing returns in the number of units in terms of both average distance from home and the proportion of patients living within a target distance (Figure 49). There were diminishing returns in adding more units. With 10 units, the average travel distance is 35 km and 50% of parents are within 25 km (straight line) of their closest unit. With 20 units, the average travel distance is 24 km and 69% of parents are within 25 km of their closest unit. With 50 units, the average travel distance is 14 km and 90% of parents are within 25 km of their closest unit. With 100 units, the average travel distance is 10 km and 97% of parents are within 25 km of the closest unit. With 150 units, the average travel distance is 7 km and 97% of parents are within 25 km of the closest unit.

FIGURE 49.

Relationship between the number of units and the distance parents are from their closest unit. The solid line shows the average distance (straight line) and the dashed line the percentage of patients within 10 miles (straight line) of the location of care.

Methods

The NHS costs were based on the level of care received and compared with micro-costing based on nurse grade. The costs to families included travel time, vehicle operating costs and overnight stays. Preliminary predictions of infant survival were based on infant characteristics (gestation, gender and birth weight) and workload of the neonatal units29,37 (methods are reported in Chapter 8, Mortality model).

The economic evaluation was based on a fixed cohort of pre-term infants and their referral pathway in the Peninsula network and beyond. The model was run over a 60-year period to increase precision (i.e. reduce the confidence intervals around the means) (see Chapter 8, Model run time, for more details on precision) and there was a short run-in time of 100 days before data were recorded. The referral pathway of infants outside the network, including time spent in IC, HDC and SC, and transfers were based on the observations of the Peninsula network and matched to similar hospitals outside the network (ones that have similar hospital characteristics).

To investigate the impact of centralisation and unit closures, a total of seven scenarios were modelled (Table 33). Under these scenarios we were able to look at the impact of moving infants from localised to centralised care in the Peninsula network using network on-duty staff (the number of infants seen outside the network is constant across scenarios). Scenarios 1–7 moved progressively from localised (all facilities exist locally) to centralised. In scenario 7, where all network neonatal units apart from the network ICU have been closed, we have made the realistic assumption that hospitals still provide interim care to stabilise infants born locally, even when they no longer have a neonatal unit (assumed to be half a day of care). We also estimated the costs and outcomes of stepwise moves towards greater compliance with BAPM workload guidelines based on the current system (scenario 4). As previously explained in the report, a unit may no longer admit new infants when the workload exceeds a given proportion above BAPM recommended workload. At a threshold of 100%, hospital workload may never exceed the BAPM recommendations. We also investigated the impact of greater compliance with workload guidelines, moving from restricting workloads to an upper limit, for example 200% of BAPM guidelines.

Predictions of NHS costs were based on two sources: the level of care received and a micro-costing based on WTE nurses. Unit costs for the level of care received were based on national reference costs 2011/12 and unit costs of WTE nurses are based on Curtis 2012. 38 Although each band of nurses has a range of salaries attached to it, Curtis bases the cost on the medium salary within each band (i.e. the median full-time equivalent basic salary for Agenda for Change Bands of April–June 2012 NHS Staff earnings estimates for Qualified Nurses). Table 34 summarises the unit costs applied in the economic evaluation.

The costs to families that we included were travel time and vehicle-operating costs. The unit costs applied to travel time were based on the Department of Transport non-business travel costs. 39 Travel for non-business was based on a study estimating the willingness to pay of travellers for shorter travel times. 40 This study found that travellers gave the same value to time in all modes of transport. The Department of Transport cost business travel at a higher rate, which varies between modes of transport based on lost workplace productivity and the wages of employees. We did not apply these higher costs here, although it is recognised that travel may also take place in work time for partners of women who have undergone a caesarean section (and are recommended to not drive in the first 6 weeks), as the partners may take time off to drive mothers to the hospitals. The unit cost of vehicle-operating costs was based on the AA motoring costs41 and included an allowance for car parking.

Results

When the model was run for 60 years this produced a cohort of 168,782 pre-term infants, and we used this cohort to look more systematically at changes in the referral pathway.

Most units in the Peninsula region are working at thresholds of closure above the BAPM recommended ratio of nursing staff to infants. In Tables 35 and 36 we modelled the impact of restricting workloads to a maximum of 200%, 190%, etc., so that the hospitals became increasingly compliant with the BAPM. Table 35 shows that transfers between hospitals and parental travel increased as the threshold to closure was lowered, and Table 36 shows that the mean NHS and family costs increased. We also explored the impact on mortality, and found that, as the threshold is lowered, mortality falls. For example, movements from 140% to 110% and 100% reduced the probability of mortality from 1.07 to 1.02 to 1.01, based on a preliminary mortality model outlined earlier (see Chapter 8, Mortality model). A key driver of mortality in this model was adherence to the BAPM, so in this case lowering BAPM thresholds increased costs but also reduced mortality. This result raised the question of whether or not the increasing costs are worth the improved outcome, to which we return in the discussion. However, this situation may no longer arise if we also take into account the positive effect that centralisation may have on mortality. 33

We looked at the impact of moving from localisation (scenario 1) to centralisation (scenario 7). We considered the impact of centralisation under two situations: (1) removing the BAPM closure threshold, so that units never close to new admissions, and (2) setting the BAPM threshold at 200%, the level typically found in one of the central referral hospitals in the network. For this analysis, we looked at the cost implications only. Table 37 reports the impact of centralisation when the BAPM threshold is removed, and shows that movement to centralisation increased the mean distance and time travelled by parents. Table 38 shows the costs. Unit closures also increased mean distance and time travelled by parents. Table 39 reports the impact of centralisation when the BAPM threshold is 200% and shows similar findings.

A more accurate reflection of cost variation, as the system moves towards greater centralisation or unit closures, is found by looking at the network on-duty staff, a figure which is more able to reflect the changing cost structure that arises from changing the referral pathway. The analysis assumed that infants are not transferred because of cot or nurse shortages and that all transfers are in line with expected location of care. Table 37 shows the numbers of network on-duty staff to meet BAPM standards 80%, 90% and 95% of the time. Table 40 shows WTE staff costs per admitted infant based on the assumption that 70% of registered nurses are qualified in specialty (following markers for good practice3). In Table 40, mean staff costs per infant fell with greater centralisation and unit closures.

Model challenges and validation

Demand levels and geography

In 2007, the DH reported that 68,000 infants were admitted into neonatal units,3 19,500 of whom required IC. According to the ONS there were 655,357 live births in that year. 22 This equates to an admission rate into neonatal units of 10.4%, and an admission rate into IC of 3.0%. In our study area we found that total use of neonatal units (≈ 15% of births) was higher. Using the BAPM 2001 guidelines (which are the ones appropriate for the year of the DH report) we also found that admission into IC, at ≈ 4% of births, was higher than the national average.

There is no direct indicator in the data about why demand in the Peninsula region should be higher than the national average. One possible contributing cause may be the demographic characteristics of the region. As the majority of the Peninsula network has a deprivation index score higher than the national average, it is possible that the economic characteristics of the region contribute to the higher than average admission rate into neonatal units, because the risk of pre-term or low-birthweight infants has been linked to deprivation. 43–45 The highest admission rates (> 20% of births requiring neonatal care) were focused around the cities, especially Plymouth, where admission rates of 30% of births or higher were found in more than half the city centre GP practices. The inner-city Plymouth area is also in the highest quartile of deprivation scores. However, from our data we cannot say that the link between higher admission rates and the local deprivation score is causative, and there may be other contributing factors.

In our analysis, we compared a model based on births per GP with a model based on admissions into neonatal units. We found, when comparing them with observed data, both models to have similar predictive power. The observed variation in admission rate in and around the inner-city areas did not seem to affect the model adversely, possibly because these differences become averaged out over the wider catchment areas for those hospitals. We considered that using Badger admissions had the sounder theoretical basis, despite not actually improving model fit, and therefore continued to use neonatal admissions (rather than births) per GP as the basis for all other analysis within the Peninsula network. Using GP practice births as an indicator of demand does, however, appear to be reasonable when direct demand data are not available.

Workload under British Association of Perinatal Medicine 2001 and 2011 guidelines

In 2011 the BAPM updated its 2001 standards for classification of neonatal care into four levels. 7 We have used the 2011 standards throughout our modelling. When considering the number of nurses required, the use of BAPM 2001 or 2011 standards can make a significant difference. The difference between 2001 and 2011 categorisation is complicated a little by the introduction of a new category of care, namely ‘Transitional Care’, in the 2011 categorisation. The main characteristic of TC is that ‘the mother must be resident with her baby and providing care’. There are no guidelines for staffing of these infants and we have used a nominal eight infants to one nurse when calculating workload including TC (using this level of nursing, TC infants contribute ≈ 4% towards total workload).

Overall we found that calculating required staff levels using BAPM 2011 standards led to a reduction of 10–15% compared with using BAPM 2001 standards (the greater reduction is where TC infants are removed from the calculation). The effect appeared to be greater on the two SCUs than the LNUs or the network ICU. This might be significant if the change from BAPM 2011 to BAPM 2001 affects remuneration; the income would be rebalanced a little differently when using the two different sets of guidelines.

Variation in workload and staffing levels

A key issue when planning required health-care resources is recognising and planning for variation in unscheduled demand. Bagust et al. 5 suggested that in a 200-bed hospital an average bed occupancy of 85% would be an appropriate planning target to allow for normal variation in emergency bed requirements, with bed occupancies above 90% leading to frequent bed crises. Queuing theory predicts that as the number of arrivals increases the relative variability reduces. 46 This is reflected in the Poisson distribution, which models the variability of arrivals in any given unit of time. The Poisson distribution predicts that the standard deviation of the mean arrivals in any given unit of time is equal to the square root of the mean. Thus, if a unit experiences 100 arrivals a month, on average, the standard deviation in arrivals is expected to be 10, or 10% of the mean. However, a smaller unit, experiencing 25 arrivals a month, on average, will be expected to have a standard deviation of arrivals of five, or 20% of the mean.

When examining the variability of workload in this system we did indeed see the higher variability expected of low-throughput systems. For example, the typical trough and peak (10th and 90th percentile) number of IC infants in Plymouth ranged from one to six, with an average of three. Even across the network there is significant variation, with trough and peak infants in IC ranging from 3 to 10 (average 6). Variability reduces with the higher-throughput care levels, with a trough of seven infants in HDC and a peak of 16, a trough of 36 infants in SC and a peak of 53, and trough of all infants of 61 and a peak of 80. Peak occupancy and workload (90th percentile) were more than three times the trough (10th percentile) in the two SCUs, and 1.5–2 times the trough in the larger units.

Across the network as a whole, workload ranged from 20 (trough) to 28 (peak) nurse equivalents (average 24). This compared with an average number of on-duty staff of ≈ 28 nurses (excluding managerial and transport nurses). The network as a whole was working at 85% occupancy if maximum occupancy was set according to BAPM guidelines. The challenge in this system is balancing demand with staffing across a wide area. Although the system theoretically had sufficient nurses to meet BAPM standards 90% of the time across the network as a whole, in reality the system was stressed: Plymouth ran on average at 30% more workload for the nurses available than the BAPM standards recommend, and was above BAPM guideline workload for 85% of days. Being the unit in the network providing the highest level of care, Plymouth has the least flexibility; there are infants who can be cared for only at Plymouth. It might be argued that the units providing the highest levels of care therefore need the most spare capacity, as when they can no longer take in the sickest infants then the network as a whole closes to new admissions for these infants. If all units were resourced to cope with 90th percentile peak workloads (with unplanned transfers), as experienced in the observed data, then ≈ 34 on-duty nurses (excluding management and transport nurses) would be required at any one time. The model therefore predicted that, to meet BAPM standards 90% of the time, 35 on-duty nurses would be required if each unit were staffed to cope with local variation in workload. The model aligned with observed data and predicted that there is currently ≈ 20% shortfall in network nursing to cope with the local variation in workload. The nursing shortfall is currently most severe in the network ICU, Plymouth, which as highlighted above should arguably be where the highest spare capacity is available.

In the face of a local shortfall in resources, a key advantage of the neonatal network system is that infants can move to where capacity is available. There is a price to be paid for this flexibility, however. As the network capacity is increasingly filled, more infants become displaced from the location of first choice. The model predicted, for example, that the number of transfers would double between 60% and 80% maximum permitted workload (assuming maximum capacity is 150% BAPM workload) and would double again as workload rose from 80% to approaching 100% maximum permitted workload. At the same time, the average distance parents would have to travel would rise from 22 km to 25 km and then 34 km at 60%, 80% and 100% maximum permitted workload, with 7%, 11% and 19% of infants over 50 km away from their parents’ home location.

Another key factor influencing the number of transfers and the travel distances for parents is the workload level at which units close to new admissions. If the network is resourced to meet average workload according to BAPM guidelines and units could work up to 150% BAPM workload (workload 50% above BAPM guidelines) then ≈ 600 transfers would be expected per year. If, however, units were to close to new admissions when workload is 20% above BAPM guidelines, then the number of transfers would increase threefold to ≈ 1800.

Localisation versus centralisation

One of the key aims of the model was to examine the effects of altering configuration of services. We were especially interested in exploring the effect of moving between localised and centralised services.

There are clinical reasons why centralisation of services may be beneficial. Studies in both the USA32 and the UK33 have shown that units with more admissions have reduced mortality rates for their given gestational age intake. In the UK study the largest contributor to reduced mortality was considered to be the more aggressive use of prenatal corticosteroids. In this modelling study, though, we focused on investigating the operational effects of centralisation.

The potential operational gain of centralisation is in reducing the spare capacity needed across the network. Queuing theory suggests that lower admission numbers lead to greater relative variability in infant number and workload. 46 As work becomes centralised there should be a reduction in relative variation (peak to average workload ratio becomes reduced). This is indeed what we observed in the modelled scenarios. The effect, however, is expected to be relatively modest in the Peninsula region when looking at likely possible options of reconfiguration. For example, downgrading the care offered at Exeter and Truro to a maximum of 2 days in IC, and closing the two smaller units, leads to a maximum 10% reduction in the staff required to meet BAPM standards 90% of the time. This model, however, assumed that no care at all was supplied in the two smaller units. If these were still centres for childbirth then, in practice, some neonatal care would likely be needed to be retained in these units, and this would reduce the potential savings to only about 4% of nurse establishment if neonatal staff must be maintained to care for those infants who need stabilisation or who are waiting for transfer. Alternatively workload could be absorbed by the maternity postnatal services. The model highlighted that neonatal unit planning needs to be performed alongside planning for where women give birth.

As closure of units is an unlikely option, we examined the effect of centralisation on IC and HDC services in more detail. It may be argued that SC, as it is available in all district hospitals, is not truly a specialised service.

There are currently three hospitals in the Peninsula region offering IC and HDC. We classified Exeter and Truro, from Badger data, as LNU+; they generally follow the guidelines for LNUs, but have cared for infants in IC for up to 28 days (see Chapter 8, Hospital types, for more details). We found that downgrading Exeter and Truro to LNUs (providing a maximum of 2 days’ IC) did not reduce the expected total number of IC or HDC cots or nurses needed. An improvement in efficiency was achieved in the model only if Exeter and Truro were downgraded to SCUs; in this case the total number of IC and HDC cots required to meet BAPM standards 90% of the time (without the requirement for unplanned transfers) could be reduced from 26 to 23 (12% saving) and the number of on-duty nurses required to care for infants in IC or HDC could be reduced from 19 to 16 (16% saving). Increasing the capacity of Exeter and Truro to network ICU status would, however, require an increase in network resources from 26 to 29 IC and HDC cots (an increase of 12%) and an increase in the number of on-duty nurses from 19 to 21 (an 11% increase). The distinction between LNU and LNU+ therefore appears to make little difference, but the network becomes more efficient as it moves from three hospitals having full IC (without surgery) to one network ICU and two LNU(+)s, and finally to one network ICU with the other units becoming SCUs. Although downgrading Exeter and Truro to SC level might reduce the required network resources, there would be a significant impact on parents. With the current configuration, the model predicted that 85% of parents are within 50 km (road distance) and the average travel distance is 28 km. If Exeter and Truro were downgraded to SCUs then only ≈ 40% of parents with children requiring IC and HDC would be within 50 km of the location of care, and average travel distances would be ≈ 55 km. The number of planned transfers would also double from ≈ 350 to ≈ 700 per year. Cot and staff savings would therefore be offset by increased transport costs and the effect on the parents.

It should be noted, however, that the Peninsula region has a very high average distance between neighbouring units. In this region the average distance between neighbouring units is 60 km by road, which compares with a national average of 22 km, and an average in London of ≈ 8 km (although travel speeds will probably be lower in London). In the whole of England, Truro is the neonatal unit that has the greatest distance (90 km) to the next nearest neonatal unit. It is likely that in other regions consolidation might be used to achieve some gains in efficiency without as great an effect on the distance from the parents’ home to location of care.

Consolidation of units, as noted above, potentially requires significantly more transfers (unless births are also relocated). Data from the mid-2000s showed that transferring infants could be inherently dangerous, with critical incidents occurring in about 15% of transfers. 47 Over a 10-year period in the former Trent Health Region, 23% of transfers were found to be unplanned. 48 In a more recent study, however, circumstantial evidence suggests that transfer risks are quite small. 33 The potential risk of transfers should be considered when planning configuration of networks (although it may be that the risk has been reduced in recent years).

An alternative to providing capacity at each hospital to cope with peaks and troughs is to use transfers to relocate an infant to a hospital where there is capacity. This is an advantage of a network-based infrastructure as exists in neonatal care. The modelling indicated, however, that this approach can come at a significant cost: network transfers rose from a minimum of ≈ 350 per year up to about 2000 per year when the network was operating at 95% capacity. As the network capacity approached 100%, the chance that the right cot was available in the right place diminished. Not only is there the cost of transferring the infant to the hospital with spare capacity, but that infant is likely to be later transferred back to the hospital of choice; two additional transfers are frequently required each time local capacity is saturated. Transfers started to rise significantly when workload rose above ≈ 70% network maximum permitted.

When considering the benefits of centralisation, much focus has, understandably, been given to the physical health outcome for the child (e.g. see Marlow et al. 33). The organisation of neonatal provision, however, may also affect the health of the mother. Mothers whose children are placed in neonatal care units experience stress and anxiety. 49 If centralisation leads to a physical separation of mother and infant then the mother may experience increased anxiety or depression. 50 In addition, there are many examples of potential benefits to the infant in keeping mother and infant close together, especially through skin-to-skin contact. 51–55

Extended model

During an extension to the project we were able to modify the model to work with any number of networks. We ran the model for the combined Peninsula South West network with or without dividing the network in two. The centralised/localised scenarios in the extended network showed the same pattern of reducing nurse requirements coupled with increasing parent travel distances and infants transfers as with the Peninsula network, although the average parent distances were notably reduced in the wider region (68% of the workload of the combined network comes from the non-Peninsula region). There was about a 20% reduction in required nurses between fully localised and fully centralised services, although the practical range (likely possible reconfiguration scenarios) within this is significantly smaller.

It is possible that, operating as two networks, parents living close to the boundary between the networks might have to travel further to a hospital in their own network than if they could simply travel to the closest hospital. We were interested, therefore, in looking at the effect of removing this artificial boundary. The modelled results, however, showed very little impact overall of removing the boundary. It could be that we underestimated the effect by looking at a single network boundary; there were relatively few births close to the boundary in the model. It would be more interesting to examine this effect at a national level, as for a complete model a higher proportion of patients might live close to a network boundary and so might be displaced further than they need be if the closest available suitable cot is in a network other than their own.

Public transport

The model was based on fastest-route road distances from a parents’ home location to the different hospitals in the network. During discussion with parents the point was raised that parents might use public transport in place of private car travel. Although this is unlikely to significantly affect which hospital is the closest, there is a significant difference in time taken. Public transport travel times overall were about twice that of private car travel (these times reflect the travel time and waiting time within the travel only; they do not reflect parking time, for example). The most likely significant effect of the longer public transport time is in estimating the number of patients who are above a reasonable travel distance each day. In this report we have reported the proportion of parents above 50 km (road distance) away from the point of care; this represents approximately 1 hour’s travel time each way in our study region. By assuming private car travel for all parents we may therefore be underestimating the proportion of parents beyond a reasonable travel time. When planning accommodation for distant parents it may be prudent to conduct some more research into the use of public transport by parents.

Mortality modelling

The mortality module was an exploratory addition to the main model, looking to link an operational model to outcomes, and was not generally used. The predicted mortality (1.03%) was very close to the observed mortality (1.02%). We believe, however, that caution is required for this module. The mortality was based on infant characteristics30 and also unit occupancy29 as a percentage of a nominal maximum level. Infant characteristics are relatively easy to quantity whereas unit utilisation is more complex; utilisation may be expressed as a percentage of a nominal number of infants, as Tucker et al. 29 used in their paper, or as a percentage relative to a maximum workload for any given number of nurses. We have used the latter in our model, with a maximum workload of 150% BAPM guidelines being a nominal maximum from observation across the network. Changing the level that is considered 100% would, however, alter the predicted mortality. In addition, studies in both the USA31 and the UK33 have shown that units with greater numbers of admissions have lower mortality rates for their given gestational age intake. This effect may be due to greater specialist skills, equipment or knowledge, or to a more aggressive approach (such as greater use of prenatal corticosteroids). Our mortality module did not include this component. Mortality might also be influenced by differences in treatment between units. However, it was not within the scope of this module to predict treatment-dependent mortality rates; we were limited to understanding how unit operational characteristics might affect mortality.

With those caveats emphasised, the mortality module did demonstrate how changing workload within the network might lead to altered mortality levels. Between 50% and 100% utilisation, the model predicted mortality rates of 0.9–1.2% for the Peninsula network. Mortality occurs among the sickest infants and it was notable that Plymouth, the unit that takes the sickest infants, also ran with the highest nurse utilisation. There might, therefore, be potential for reduced mortality by reallocation of resourses so that the network ICU runs with the same or lower utilisation than the other units.

Single hospital versus network modelling

One of the aims of the project was to compare network modelling with single hospital workload modelling. It was clear from the modelling here, such as the localisation versus centralisation, that a network model offers key advantages over a single hospital workload model. Examples where a network model are essential include:

• Closure or downgrading of one or more hospitals: In these scenarios workload may be distributed between two or more other hospitals with the balance of workload depending on the geographical location of the population. In the Peninsula network, for example, hypothetical closure of the SCU at Torbay leads to increased workloads in both Exeter and Plymouth.

• Altering capacity at a hospital: Reducing capacity at one hospital may lead to changes in workload at two or more other hospitals, as the unit may close more often to new admissions and the work must be transferred elsewhere.

• Parent travel distances, and transfer numbers and distances: To understand likely parent travel distances (and the number of parents who are beyond reasonable daily travel distances) it is essential to know both the location of parents and hospitals in a network. Without all regional hospitals present it is not possible to work out which suitable cot is closest to the parent. Likewise, transfer numbers and distances depend on an understanding of how infants must move about the network for different levels of care (or because of capacity shortages).

Health economics

We present preliminary work on the economic evaluation of referral patterns arising from a network model of neonatal care. Preliminary analysis showed that greater centralisation achieves cost savings from more efficient managerial structures, but imposes greater costs on families. Greater achievement of BAPM guidelines reduces mortality, under the model of mortality that we have used, but at a higher financial cost.

In these preliminary analyses we have been conservative in our estimates of family costs. For example, our estimate of family travel costs is well below those estimated by BLISS for families of neonatal care (BLISS estimated £400 for travel and £125 for car parking in 200656). BLISS based costs on all forms of transport, including public transport. In our model we estimate that basing travel on public transport would approximately double travel times. There are also other costs that are not yet incorporated into the model, including accommodation, child care and lost earnings. In February 2014, BLISS completed a nationwide survey of family costs, which gives additional disaggregated data on mode of travel and lost earnings, and which we will aim to include in further analysis. For example, there is also the possibility of including overnight stays for those in the early stages of care, or who live far from the hospital, with knock-on effects on child-care costs. Future work is needed on the major cost drivers for families and how they vary by urban/rural setting, family size, mode of transport and socioeconomic groups. The aim here would be to develop an algorithm to determine family costs of increased journey time, child-care costs, lost earnings and overnight stays due to neonatal care.

The preliminary analyses have raised some questions about the best approach to evaluate changes in referral patterns. Changes in service configuration affect the unit cost of bed-days, so applying a standard cost per IC infant seems oversimplistic. This suggests a need for greater clarity in the components that make up national reference costs for neonatal care bed-days, in order that we can adjust these figures accordingly to reflect changing unit costs and then apply these costs to episodes of care.

We are also concerned that the effects of closing units are likely to be non-negligible and the expected saving from disinvestment is not always achieved. 57 Moreover, in hospitals, services are provided jointly and closing one service has potential knock-on effects for other services. For example, hospital maternity and paediatric services in the South West currently use neonatal units for infants requiring stabilisation. If a hospital no longer provides neonatal care it will be necessary still to provide this service, for example through a practice nurse (band 8). But is it that simple? There are clear cross-subsidies of other staff across the maternity and neonatal services, including midwives, which would suggest the need for a model that captures both maternal and neonatal care jointly. In addition, when considering changes to configuration we have also not considered the capital costs of establishing units; our costing is for ongoing operational costs only.

There is a potential mismatch with our model and the secondary data sources of mortality. We assume that mortality occurs to infants in IC only, which we see in the Badger data set. In contrast, Manktelow et al. ’s estimates of mortality are for infants entering NICUs before 33 weeks and where there may have been mortality among the older of these infants in HDC. 30 Similarly, for the relationship between occupancy and mortality, it is not clear to which levels of care the Tucker et al. 29 occupancy measure should be applied.

Finally, there is the overriding problem that there is no clear decision rule for whether or not the additional costs justify the additional benefits using mortality estimates. Changes in service provision will affect more than one outcome: centralising services may reduce the risks of mortality for the very premature, but the costs to families can be disproportionate. Moreover, there are knock-on effects for the longer-term development of the infant, with associated uncertainties over morbidity. More work is needed to explore the factors that policy-makers and families would like to see taken into consideration in determining service reconfiguration. We need to explore how we might weight these competing outcomes, based on the views of families and/or members of the general population.

There are some limitations to the analysis. The relationship between occupancy and mortality was taken from Tucker et al. 29 but the definitions of levels of care and BAPM guidelines have changed since the time of their study. Little is reported about how best to combine the workload measure into a model of infant mortality. For example, Tarnow-Mordi et al. did not report on whether or not the coefficients applied to the infant characteristics change when the workload measure was added.

National modelling

Although it was not within the original scope of the project, we undertook some high-level analysis of the location of units at a national (England) level.

There was a significant difference in the distances between neonatal units. The average distance (by road) between neonatal units was highest in the Peninsula region. The average distance between closest units was 60 km in the Peninsula region, compared with 23 km nationally and about 8 km in the London networks. As noted in the discussion of centralisation versus localisation, the differences in geography are likely to affect how feasible consolidation of care is, without adversely affecting parent travel distances unacceptably.

A ‘greedy algorithm’ was used to investigate the relationship between the number of units (of any particular type) and the travel distances for parents. There were, as expected, diminishing returns in adding more units. Average travel distances and the number of people within 25 km of a unit improve rapidly up to about 50 units. Some moderate improvement is made between 50 and 100 units, and then only little improvement is made between 100 and 150 units.

It should be noted that there may be other good reasons for having more units than this algorithm suggests. For example, capacity limitations may prevent full consolidation; many hospitals are limited in how much they may expand and take on work currently performed elsewhere.

We have not listed here the order in which the algorithm picks locations, for two main reasons. Firstly, this algorithm does not use the current level of specialism within a unit; it does not differentiate between current IC and SC units when deciding where the next best location would be. This is unlikely to be simply an ‘academic’ problem with the algorithm; ICUs are likely to be best placed in hospitals that are well equipped with other specialist services. It is more likely, for example, that laboratory tests are available for more of the 24/7 period in a major hospital. The second reason we have not listed the order of the units is that the order in a greedy algorithm is very dependent on which locations have previously been picked. As an example, two hospitals may be very close together, such as Southmead and St Michael’s in Bristol. The advantage of choosing one over the other, if neither has been previously picked, may be very small and the algorithm will pick one by a potentially very small margin. The other hospital then can have little effect on the overall score, as placing a second hospital within Bristol is not going to significantly affect average distances nationally or the number of people within 25 km. The second hospital may then be pushed very much lower in the preferred list of locations, making it appear a poor location for placing a hospital. A better way of interpreting these results would be to see Bristol’s location in the list; the choice between two hospitals (if consolidation is wanted) should then be made on a much wider range of decision inputs, which would include position relative to local population as just one input.

Notwithstanding these caveats on the naming of hospitals in the preferred order, the value of this algorithm is in helping inform decisions on how many units of each type are desirable, and their general geographical locations. We see this algorithm as the basis of a more sophisticated algorithm which could take other factors into consideration, such as estimated travel times (rather than distance), predicted demographic changes and the ability of the hospital infrastructure to sustain a 24/7 ICU.

Comparison with previous neonatal models

A number of neonatal care models have been published in recent years. Ours, we believe, is the most detailed model to date, reporting on how the system behaves from the perspectives of the network, each unit, the transfer team and the parents. It should be noted, however, that ‘most detailed’ does not necessarily mean ‘better’. A model should be as simple as the question being addressed allows. The different models described in the literature are each answering specific questions, and the ‘best’ model depends on the question being addressed.

Asaduzzaman and co-authors have published a series of queuing models of either a neonatal unit or a network. 12–16 Compared with simulation models, queuing models generally use more simplifications and run much faster. The higher degree of simplification has benefits and drawbacks. The major benefit, apart from computational speed, is that, because the model avoids fine detail, the model user is forced to focus more on the high-level ‘big picture’ behaviour of the system without being drawn into what may often be minor or pedantic questions. This may, however, also be a drawback, as the mathematic models described do not, for example, allow surgical/cardiac infants to be separated out, admission rules to be based on gestational age of infants, or capacity to be limited by a combination of cots and nurses. In addition, resources are not shared between levels of care. The investigation of distances between the parents’ home and the location of care was also outside the scope of these queuing models. The simplified queuing model was, however, sufficient to estimate the probability of rejection of new admissions or changing infant state, a key performance indicator for neonatal units.

During the course of our work, Fournier and Zaric published a DES study of neonatal IC in British Columbia. 17 The neonatal care aspect of their model was similar to ours; the infant moved through a series of levels of care, and the closest suitable cot, if one was not present in the home hospital, was found. Distances were based on distances between hospitals. The model had two levels of units: those providing ‘Ordinary Care’, and those also providing IC. The authors then investigated how changing the number of cots at each care level affected both cot utilisation and the number of transfers out of the region. The basis of the model was, therefore, similar to ours. There were, however, some differences. Length of stay, rather than being sampled from a distribution as in our model, was based on a Markov chain, with the probability of requiring another day of care being assessed daily. In our model we generally calculated capacity depending on nurse workload rather than specific cots being occupied. Given the nature of different types of units in the UK, it was important to separate infants by gestational age; this was not a requirement in the Canadian model. We also incorporated surgical procedures and stay as a distinct phase that could be carried out at only a limited number of hospitals (more limited than the number offering IC). These differences might or might not have had a significant effect on the model, and some of the differences simply reflected differences between the Canadian and UK models of care. The major difference between our model and the British Columbia model was in the objective of our model to analyse transfer and parent travel distances in detail.

In 2014, Demir et al. 18 published some modelling studies using techniques that had not previously been applied to neonatal care. They demonstrated a Coxian phase-type distribution based on a three-phase continuous-time Markov chain matrix. Rather than using data describing lengths of stay in specific levels of care, the number of phases and the probability of transfer between the phases are found by ‘best fit’. This technique has the advantage of fitting complex LoS distributions without the need for data describing known phases within the LoS; the phases are derived from data giving just the total LoS. The disadvantage of this approach is that the phases do not map to specific levels of care. In a study where it is important to distinguish between levels of care, this type of analysis has limited use. The authors went on to apply a multinomial random-effects model as detailed by Adeyemia et al. 58 In this model infants were assigned to various routes through three care levels. As with our modelling, the authors assume that infants progress from worse to better, although they may die and exit the system at any care level. Total lengths of stay were then assigned to each of the possible paths through the system. As with the Coxian phase-type distribution model, this model did not describe LoS at each care level, so this method would again be limited where spells at each care level needed to be distinguished. Finally, the authors described a systems dynamics model. This had the potential to examine flow through different levels of care, and the authors introduced an interesting concept of a component of LoS that is resource dependent, and a separate component that is resource independent and is dependent only on the infant (assuming no limiting resources). The authors used this type of modelling to better understand how reducing lengths of stay at different care levels might affect other parameters such as the number of infants refused entry to the unit, and they uncovered some counterintuitive results such as reducing lengths of stay leading to increasing rates of rejection of admission of new infants. A final limitation of the study, the authors acknowledged, was that the analysis was limited to a single site and did not capture the complexity of infants moving between hospitals in a network. That paper, however, helps to illustrate how different modelling techniques may be of use in answering different questions regarding neonatal care provision.

Opportunities for further work

Extension to maternity services

A second opportunity is to expand the maternity services aspect of the model. In our scenarios we explored some options where large numbers of births occurred in hospitals without neonatal services. This drove up the number and distances of transfers and left open the question of how short-term neonatal care is provided, and by whom, in those hospitals. There is potential to develop the link between labour ward and neonatal care further in the model, for example by inclusion of midwife-led birthing centres that are separate from hospitals with neonatal care facilities. This extended model would build on both this model and previous work by one of the present authors modelling labour wards. 60 We hope such an extension might provide a useful shared framework to inform discussion of how maternity services and neonatal care strategies influence each other.

Refinements to model

Hospital-level statistics on parental distance: The model currently reports the average travel distance and the proportion of all parents over a given distance away from the unit. In a subsequent model we would summarise these results for each hospital.

Neonatal transfer services: In the current model, neonatal transfer was not a limiting resource. We modelled in a fixed delay for transfer, but this was not a limiting factor in movement of infants. We modelled the number of transfers and the total transfer distance between hospitals (assuming a return journey from the higher-level hospital). There is potential to add further detail, for example having a certain number of neonatal transfer hub services (which might or might not be colocated with specific hospitals). Transfers would then be dependent on the availability of the transfer services, and the model could report the utilisation of these services along with the average wait time for transfer.

Delays in transfer: The raw data output from the model contains information on how long an infant was in a distant hospital when he or she was clinically suitable for transport back to a hospital closer to home. We shall add a refinement to the model to summarise this information.

Availability of model

As part of the project we intend to make the model available to any network. Preparing data for the model is not, however, a trivial process. Our preferred route is for a national modelling project in which we shall prepare data for the whole of England simultaneously, so all networks will be represented and results may be segregated by network. This also has the advantage that likely internetwork transfers will be represented in the model and that network affiliation may easily be changed.

Contribution of authors

Dr Michael Allen (senior modeller, healthcare modelling) was the technical lead, built the simulation model and was the primary author of the report.

Professor Anne Spencer (associate research professor, health economics) conducted the health economics analysis.

Dr Andy Gibson (senior research fellow, public and patient involvement) was responsible for parent involvement.

Mr Justin Matthews (associate research fellow, healthcare modelling) conducted the analysis of Badger and associated data, provided necessary parameters for the model, tested the model and performed scenario analyses using the model.

Dr Alex Allwood (consultant neonatologist, Derriford Hospital) assisted with the conceptual framework of the model and reviewed model outputs.

Mrs Sue Prosser (senior neonatal nurse, Royal Devon and Exeter Hospital) advised on neonatal unit working practices and assisted in parent involvement.

Professor Martin Pitt (associate research professor, healthcare modelling) was Principal Investigator and reviewed/amended the report.

Additional assistance

With thanks to the Peninsula and South Western neonatal network team for steering and guidance, especially:

• Heather Burden.

• Diana Cargill.

• Rebecca Lemin.

• Dr Vaughan Lewis.

• Dr David Mabin.

• Dr John Madar.

• Dr Rebecca Mann.

Dr Brad Manktelow (statistician for the Trent Neonatal Data Set, Leicester University) assisted with access to data from the Trent Neonatal Data Set (although, ultimately, this was not used in the modelling).

Disclaimers

This report presents independent research funded by the National Institute for Health Research (NIHR). The views and opinions expressed by authors in this publication are those of the authors and do not necessarily reflect those of the NHS, the NIHR, NETSCC, the HS&DR programme or the Department of Health. If there are verbatim quotations included in this publication the views and opinions expressed by the interviewees are those of the interviewees and do not necessarily reflect those of the authors, those of the NHS, the NIHR, NETSCC, the HS&DR programme or the Department of Health.

Example of Badger data for three patient spells