Mortality
First, we consider a series of survival models considering different definitions of low staffing in order to explore the relationships between the staffing levels experienced by patients and their hazard of death. Given that mean staffing levels are empirically derived and correlate highly with the required hours per day estimated from the ward establishments, we use staffing below the mean of the ward the patient was on for each day as our starting point. For RN HPPD, this represents staffing marginally below the estimated establishment on most wards. We then consider alternative thresholds (i.e. staffing below 80% of the mean and staffing below the estimated establishment) and determine whether or not the effect of low staffing could be confounded with a more general ‘weekend’ effect.
We then move on to consider staffing as a continuous variable by focusing on the cumulative sum of nursing HPPD relative to ward means that the patient experiences. With these data, we explore further for evidence of non-linear associations and interactions between staff groups. Finally, we consider further the composition of the nursing staff by exploring whether or not the presence of temporary staff on the ward is associated with an increased risk of mortality.
Unless otherwise noted, all models consider the cumulative sum of staffing over the first 5 days. All models were controlled for patient characteristics and ward. Full models are presented in Appendix 5 or available on request. Unconditional coefficients for staffing outcomes associations (adjusting for ward only) are given in Appendix 5, Table 32.
Low staffing thresholds
For each day that a patient was on a ward with RN staffing below the mean for that ward, the hazard of death was increased by 3% (HR 1.03, 95% CI 1.01 to 1.06). Each day of exposure to HCA staffing below the mean was associated with a 4% increase in the hazard of death (HR 1.04, 95% CI 1.02 to 1.07). When patients were on wards where admissions per RN exceeded 125% of the mean for that ward, the hazard of death was increased by 5% (HR 1.05, 95% CI 1.01 to 1.09), although there was no association between admissions per HCA and hazard of death (HR 1, 95% CI 0.96 to 1.04) ().
Staffing below the mean during the first 5 days: hazard of death
When considering only deaths within 30 days of admission, the model coefficients were almost identical. Similarly, restricting the model to emergency admissions only had no effect on the coefficients for the staffing variables (see Appendix 5, Tables 36 and 37). Considering staffing on all days of a patient’s stay showed significant adverse effects from low RN staffing only, although the coefficients were lower (HR 1.01 per day of low RN), which is probably a product of increased risk being primarily associated with days early in the stay (see Appendix 5, Table 38).
Patients admitted at the weekend experienced an increased hazard of death (HR 1.06), but the effect of low RN/HCA staffing was unchanged. When 1 or more days of a patient’s stay in hospital were over at a weekend, the hazard of death was reduced (HR 0.58) and the effect of low nurse staffing was marginally strengthened (each day of RN staffing below the mean, HR 1.04; HCA staffing below the mean, HR 1.05). As model fit was not clearly improved by including variables for weekend admissions/stay, we did not include these variables in other models (see Appendix 5, Tables 39 and 40).
Using alternative thresholds to define ‘low’ staffing provides somewhat contrasting results. For each day that RN HPPD fell below the estimated RN establishment, the hazard of death was increased by 9% (HR 1.09, 95% CI 1.06 to 1.11). However, the effect of HCA staffing below establishment was not statistically significant (HR 1.01, 95% CI 0.99 to 1.04; ). There was no association between RN staffing below 80% of the ward mean and the hazard of death (HR 0.98, 95% CI 0.94 to 1.02), whereas HCA staffing below 80% of the ward mean was associated with an increased hazard of death (HR 1.04, 95% CI 1.01 to 1.07; see ).
Staffing coefficients using alternative thresholds
Although the models using the 80% threshold provide worse model fit (in terms of AIC and BIC), these results highlight the possibility that the staffing outcome relationships are non-linear, or that there could be interaction between the effects of RN and HCA staffing. We explore this further in the subsequent section.
Staffing levels
To further quantify the effect of variation in staffing levels, we explored the effects of absolute variations by calculating the cumulative sum of staffing in hours per patient relative to the mean for the ward for each patient for each of the first 5 days. This gives an indication of the average staffing experienced by the patient over these 5 days, relative to what was normal for each ward, effectively ‘standardising’ and allowing for variation in baseline staffing requirements.
Every additional RN hour was associated with a 3% reduction in the hazard of death (HR 0.97, 95% CI 0.94 to 1.00). However, additional HCA hours were not associated with a reduced hazard of death (HR 1.01, 95% CI 0.98 to 1.04; ). Considering only hours below the mean provided similar estimates, with a 3% increase in the hazard of death for every RN hour below the mean, but no significant association with HCA HPPD (see Appendix 5, Table 44).
Cumulative sum of staffing hours per patient above/below the mean during the first 5 days: hazard of death
The pattern of results across different thresholds, and with staffing as a continuous variable, raised the possibility that the underlying relationship was non-linear, particularly in relation to HCA staffing levels. We explored this by introducing quadratic and cubic terms for the staffing variables (hours ± mean) into the models.
Introducing only quadratic terms left the linear coefficients unchanged, giving no indication of a non-linear effect (HR 1.00 for the quadratic term for both RN and HCA hours). Introducing a cubic term produced a significant quadratic coefficient for HCA hours and a near-significant coefficient for the cubic term. RN hours coefficients were non-significant, although the linear term was similar to the linear-only model. The plot of this model can be seen in , with the full model in Appendix 5, Table 45.
Care HPPD and hazard of death: non-linear effects.
There was a slight tendency for the effect of RN staffing to reduce as staffing levels increased, but, as none of the non-linear terms was close to significance, a null hypothesis of linearity cannot be rejected. However, there seems to be some evidence that, as the cumulative level of HCA staffing (hours) experienced by a patient over the first 5 days of hospitalisation varies from the mean in either direction, the hazard of death may be increased. However, as these models do not give an improved fit in relation to the linear-only models (AIC 61920.19, BIC 62453.88), any conclusions about non-linear relationships must be made with caution.
We also considered whether there was evidence that variation in levels of HCA staffing could alter the ‘effectiveness’ of RN staffing (or vice versa) by introducing interaction terms into our models. There was no significant interaction between the cumulative sums of hours of RN staffing and HCA staffing for hazard of death. However, the interaction between these two cumulative sum variables gives no direct indication of how the staffing levels ‘on the day’ might interact, so we generated variables to indicates days of low RN staffing, low HCA staffing or both. This model provided no evidence of interaction with the sum of the coefficients for low staffing by one or the other group, yielding a combined HR very similar to that associated with low staffing by both (see Appendix 5, Table 46).
Temporary staffing
We considered the effect of temporary nurse staffing by adding a variable to indicate the number of CHPPD that were provided by temporary (bank or agency) staff. We considered three different thresholds for a ‘high’ level of temporary nurse staffing, and calculated a variable to reflect the cumulative sum of days the patient was exposed to temporary staffing above that threshold. In general, adding these variables for temporary staffing did not substantially alter the coefficients for other staffing variables, although levels of significance did alter. Temporary staffing was associated with significant increases in the hazard of death, particularly at higher levels ().
Staffing below the mean and temporary staffing during the first 5 days: hazard of death
When patients experienced days with ≥ 1.5 HPPD of temporary RN staffing, the hazard of death was increased by 12% (HR 1.12, 95% CI 1.03 to 1.21). Exposure to lower levels of temporary RN staffing was not associated with significant increases in the hazard of death. Although the hazard of death was significantly increased with exposure to days with ≥ 30 minutes of temporary HCA staffing (HR 1.05), the trend was inconsistent across the different thresholds: exposure to days of ≥ 1.5 HPPD of temporary HCA staffing was associated with a similar increase in hazard of death (HR 1.04). There was no clear benefit in terms of improved model fit compared with models without the inclusion of temporary staffing (AIC was marginally lower and BIC was marginally higher) and, therefore, we did not routinely include this variable in other models.
Summary and implications for remaining analyses
The analyses presented so far confirm a relationship between RN staffing levels and patient mortality. By taking a variety of perspectives on ‘low’ staffing, there is a largely consistent conclusion: when patients experience lower levels of RN staffing, there is an increased hazard of death. The relationship appears to be linear: the higher the average staffing levels experienced by the patient, relative to the mean for the current ward, the lower the hazard of death. The only clear exception to this conclusion comes when using a very low threshold to define low staffing; that is, when staffing falls below 80% of the mean. In this case, we have good reason to doubt the validity of staffing data as redeployment of staff from other wards to fill significant gaps is not recorded in our data sets. This is more likely to happen when staffing falls to extremely low levels. Mortality also appears to increase when admissions per RN exceed 125% of the mean for the ward. There is also evidence that increased adverse events are associated with lower RN staffing levels and that length of stay is reduced when patients experience higher staffing.
For HCA staffing the picture is substantially more ambiguous. Whereas previous research has found adverse outcomes to be associated with higher levels of HCA staffing, that is not clearly reflected here, although we did find a small negative effect on length of stay. On the other hand, there is no consistent picture of benefit. We did not see significant association between admissions per HCA and mortality. Days of low HCA staffing relative to the mean are associated with increased hazard, but there is no beneficial effect from cumulative hours and a possible non-linear effect whereby harm increases as hours of HCA staffing moves away from the mean in either direction. Although our analyses hint at potential non-linear effects and possible interactions with RN staffing levels, the nature of this relationship is not easy to resolve. Consequently, these potential effects and interactions will be explored further in subsequent analyses examining missed care to see if any light is shed on the matter.
Our secondary analyses identified that the nurse staffing effects we saw were independent of weekend effects (and so are unlikely to be confounded by low staffing from other groups) and also suggested that there were negative effects associated with days when temporary staffing levels were high.
Taking all this into account, we made a number of decisions to inform our basic approach to modelling in relation to missed care (e.g. observations, nutritional assessments and ‘failure to respond’). We dropped admissions per HCA from these models when preliminary testing confirmed that it was not a significant predictor in missed care models, just as it was not in survival models. However, our interest in a possible interaction led us to routinely include an interaction effect for the two main staffing variables (RN and HCA HHPD) because, unlike in the survival models, this variable reflected the interaction of staffing levels on a particular day or, indeed, shift.
