Specific recommendations for nurse staffing enabled comparisons between units and an examination of levels of nursing provision in relation to risk‐adjusted mortality in neonatal care.
Adjustment was made for infant illness severity using gestational age and a 12 hour probability model.25
Although larger units tended to have more immature and sicker infants than smaller units, risk‐adjusted mortality was not related to the size or type of neonatal unit. Other studies, including the UKNNSS have detected no difference in risk‐adjusted outcomes by unit size.11
Over half of the nursing shifts were understaffed, while nearly a quarter did not have the minimum number of nurses with specialist neonatal nurse qualifications to care for intensive care and high dependency infants. There was wide variation in nursing provision, consistent with previous studies of neonatal nurse staffing.19
Similarly variation in staffing levels by time of day and day of week corroborates the findings of an earlier UK survey.19
Using logistic regression specialist nursing provision was inversely related to risk‐adjusted mortality and subgroup analysis indicated that increasing the ratio to greater than 1.2 decreased the probability of mortality by 48%. In other words, providing more than the minimum recommended number of nurses with specialist neonatal qualifications significantly increased the chance of survival in this cohort.
The possibility that the relationship between risk‐adjusted mortality and specialist nursing provision could be attributed to confounding variables that were not examined in this study cannot be excluded. However, the probability is small as the approach included two primary methods of stratification not previously utilised. The first included organisational stratification by unit type and thus an attempt was made to separate the relative contributions of unit size and staff interaction. Secondly, analysis was based on infant profiles using individually determined risks, initially of illness severity and subsequently of workload demands and nurse provision representative of that infant's neonatal stay.
An important consideration is the omission of the neonatal unit as a predictive variable in the regression equation, and the independence of workload variables, calculated for each infant, which could potentially overestimate the significance of the association between specialist nurse provision and risk‐adjusted mortality. This possible effect could be determined by modelling for the 54 neonatal units. However, the ability to do so was limited by the raw event rate, which, in 15% of units, was zero. Conversely, by using data for the whole duration of infant stay, not simply the most critical period of intensive or high dependency care, it could be argued that there was a dilution of the effects of inadequate staffing.
The method for adjusting for illness severity used a probability model based on twelve‐hour data from birth, which is independent of subsequent therapeutic decisions. Although closely related to the validated and widely used CRIB score, the logistic model derivation process is designed to maximise predictive power, but runs the risk of over‐fitting the idiosyncrasies of this dataset. Thus both the probability model and the final model of risk‐adjusted mortality and specialist nurse provision, while having a good discriminatory power, lack support from independent validation.25
Adjustment for clustering, for example by use of generalised estimating equations, may have increased the confidence interval around the observed estimates of risk adjusted mortality, but is unlikely to have changed the direction of apparent effect.
This study used recommendations published in 1996 to measure adequacy of nursing levels. More recent recommendations in the UK suggest higher ratios of nursing staff for intensive care and high dependency infants.22
However, a survey of UK neonatal units conducted in 2005 showed that of 143 neonatal units, only three (2%) met the new recommendations for nurse staffing establishments and 20% were below those made earlier.23
Thus the analysis using earlier recommendations is appropriate.
The measure of specialist nursing used in this study is the ratio of nurses who have undergone specialist neonatal training, in relation to the number of intensive care and high dependency infants. It reflects the ability to meet the demands for trained neonatal nursing and supports claims that quality of care may be impaired if the availability of trained staff is too low.15
In the current nursing shortage, increasing nurse: patient ratios will be difficult. In America and Australia, one controversial initiative has been to mandate ratios for adult and paediatric care.33
Optimisation of workload planning, by developing improved workload predictors from patient characteristics is also possible.35
In neonatal care, mechanisms that allow more efficient staffing, that is the ability to flex up and flex down in the face of volume changes, are also key in addressing variable demand.37
This study adds weight to previous calls for the collection of more detailed nurse staffing data in conjunction with more reliable measures of patient acuity to better match nurse staffing and patient need.38
More effective workforce planning, perhaps involving networked care, are crucial to ensure that nursing levels match infant demands.
What is already known on this topic
- Shortages in nurse staffing in adult care have been linked with impaired patient outcomes.
- Most UK neonatal units do not meet levels of medical and nursing staffing recommended by professional bodies.
- Neonatal studies to date have provided inconclusive evidence of any relationship between nurse staffing, skill mix and infant outcomes.
What this study adds
- Risk‐adjusted mortality is related to specialist nursing input.
- Increasing the ratio of specialist nurses to intensive care and high dependency infants may increase chance of survival in very low birthweight and preterm infants.