The UK neonatal staffing study: A prospective evaluation of neonatal intensive care in the UK

A framework to address key issues of neonatal service configuration in England: the NeoNet multimethods study

Background

There is an inherent tension in neonatal services between the efficiency and specialised care that comes with centralisation and the provision of local services with associated ease of access and community benefits. This study builds on previous work in South West England to address these issues at a national scale.

Objectives

(1) To develop an analytical framework to address key issues of neonatal service configuration in England, (2) to investigate visualisation tools to facilitate the communication of findings to stakeholder groups and (3) to assess parental preferences in relation to service configuration alternatives.

Main outcome measures

The ability to meet nurse staffing guidelines, volumes of units, costs, mortality, number and distance of transfers, travel distances and travel times for parents.

Design

Descriptive statistics, location analysis, mathematical modelling, discrete event simulation and economic analysis were used. Qualitative methods were used to interview policy-makers and parents. A parent advisory group supported the study.

Setting

NHS neonatal services across England.

Data

Neonatal care data were sourced from the National Neonatal Research Database. Information on neonatal units was drawn from the National Neonatal Audit Programme. Geographic and demographic data were sourced from the Office for National Statistics. Travel time data were retrieved via a geographic information system. Birth data were sourced from Hospital Episode Statistics. Parental cost data were collected via a survey.

Results

Location analysis shows that to achieve 100% of births in units with ≥ 6000 births per year, the number of birth centres would need to be reduced from 161 to approximately 72, with more parents travelling > 30 minutes. The maximum number of neonatal intensive care units (NICUs) needed to achieve 100% of very low-birthweight infants attending high-volume units is 36 with existing NICUs, or 48 if NICUs are located wherever there is currently a neonatal unit of any level. Simulation modelling further demonstrated the workforce implications of different configurations. Mortality modelling shows that the birth of very preterm infants in high-volume hospitals reduces mortality (a conservative estimate of a 1.2-percentage-point lower risk) relative to these births in other hospitals. It is currently not possible to estimate the impact of mortality for infants transferred into NICUs. Cost modelling shows that the mean length of stay following a birth in a high-volume hospital is 9 days longer and the mean cost is £5715 more than for a birth in another neonatal unit. In addition, the incremental cost per neonatal life saved is £460,887, which is comparable to other similar life-saving interventions. The analysis of parent costs identified unpaid leave entitlement, food, travel, accommodation, baby care and parking as key factors. The qualitative study suggested that central concerns were the health of the baby and mother, communication by medical teams and support for families.

Limitations

The following factors could not be modelled because of a paucity of data – morbidity outcomes, the impact of transfers and the maternity/neonatal service interface.

Conclusions

An evidence-based framework was developed to inform the configuration of neonatal services and model system performance from the perspectives of both service providers and parents.

Future work

To extend the modelling to encompass the interface between maternity and neonatal services.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

Is midwife workload associated with quality of process of care (continuous electronic fetal monitoring [CEFM]) and neonatal outcome indicators? A prospective study in consultant-led labour wards in Scotland

Evidence for staffing recommendations in labour wards is scant. This study aimed to test association between midwife workload with adjusted process of continuous electronic fetal monitoring (CEFM) and neonatal outcome indicators. This was a prospective workload study in 23 consultant-led labour wards in Scotland. There were 3489 livebirths during September 2000, and 1561 consecutively delivered women with CEFM case review during the mid-two weeks. Process measures were: adjusted rates of CEFM, appropriate CEFM, and time to medical response for a serious fetal heart trace abnormality. Neonatal outcome indicators were: Apgar score < 7 at 5 minutes, admission to neonatal unit (NNU) > 48 hours, and neonatal resuscitation. Complete information was available for 99% (2553/2576) of workload time points, 99% (1559) of CEFM process, and 3083 eligible neonates. There were no associations between occupancy or staffing ratios and adjusted CEFM process, Apgar < 7 at 5 minutes (0.98 [0.83, 1.15]) or admission to NNU for > 48 hours (0.97 [0.95, 1.00]). However, there was association between increasing staffing ratios and lower odds of adjusted neonatal resuscitation (excluding bag and mask only) (0.97 [0.94, 0.99]). The direction of effect of increasing workload suggests detriment to outcome indicators, although the size of effect may be small.

Patient volume, staffing, and workload in relation to risk-adjusted outcomes in a random stratified sample of UK neonatal intensive care units: a prospective evaluation

BACKGROUND

UK recommendations suggest that large neonatal intensive-care units (NICUs) have better outcomes than small units, although this suggestion remains unproven. We assessed whether patient volume, staffing levels, and workload are associated with risk-adjusted outcomes, and with costs or staff wellbeing.

METHODS

186 UK NICUs were stratified according to volume of patients, nursing provision, and neonatal consultant provision. Primary outcomes were hospital mortality, mortality or cerebral damage, and nosocomial bacteraemia. We studied 13515 infants of all birthweights consecutively admitted to 54 randomly selected NICUs. Multiple logistic regression analyses were done with every primary outcome as the dependent variable. Staff wellbeing and stress were assessed by anonymous mental health index (MHI)-5 questionnaires.

FINDINGS

Data were available for 13334 (99%) infants. High-volume NICUs treated the sickest infants and had highest crude mortality. Risk-adjusted mortality and mortality or cerebral damage were unrelated to patient volume or staffing provision; however, nosocomial bacteraemia was less frequent in NICUs with low neonatal consultant provision (odds ratio 0.65, 95% CI 0.43-0.98). Mortality was raised with increasing workload in all types of NICUs. Infants admitted at full capacity versus half capacity were about 50% more likely to die, but there was wide uncertainty around this estimate. Most staff had MHI-5 scores that suggested good mental health.

INTERPRETATION

The implications of this report for staffing policy, medicolegal risk management, and ethical practice remain to be tested. Centralisation of only the sickest infants could improve efficiency, provided that this does not create excessive workload for staff. Assessment of increased staffing levels that are closer to those in adult intensive care might be appropriate.

Hospital mortality in relation to staff workload: a 4-year study in an adult intensive-care unit

BACKGROUND

Few studies have examined mortality rates in relation to the workload of hospital staff. We investigated this issue in one adult intensive-care unit (ICU) in the UK.

METHODS

We measured ICU workload per shift during each patient's stay for all admissions between 1992 and 1995 that met criteria for adjustment of mortality risk by the APACHE II equation (n=1050). APACHE II data were validated by one observer. Measures of workload in each patient's stay included occupancy, total ICU nursing requirement as defined by the UK Intensive Care Society, and the ratio of occupied to appropriately staffed beds. Over the period, staffing was appropriate for between 4.1 and 5.3 occupied beds (1.3 nurses per patient).

FINDINGS

There were 337 deaths, 49 more (95% CI 34-65) than predicted by the APACHE II equation. Median occupancy was 5.8 beds, and median nursing requirement was 1.6 per patient. On multiple logistic regression analysis, adjusted mortality was more than two times higher (odds ratio 3.1 [1.9-5.0]) in patients exposed to high than in those exposed to low ICU workload, defined by average nursing requirement per occupied bed and peak occupancy; the unadjusted odds ratio for this comparison was 4.0 (2.6-6.2). After exclusion of measures of nursing requirement, adjusted mortality increased with the ratio of occupied to appropriately staffed beds during each patient's stay. All logistic regression models fitted the data satisfactorily.

INTERPRETATION

Variations in mortality may be partly explained by excess ICU workload. This methodology may have implications for planning and clinical governance.