Getting the right staffing mix reduces hospital deaths, finds study BMJ_Open
Specialist trusts that do not provide general acute care do not report SHMI and, therefore, were not included in the study.Clinical staff are classified according to occupation codes. We grouped staffing variables by frequency and occupation as listed by NHS England, merging groups that had mean below 3 with others of a similar occupation in order to create eight groups staff).
We used the number of general acute occupied beds to calculate the bed-per-staff ratio. Beds assigned to maternity, mental illness and learning disabilities services were excluded; however, general acute beds represented 96.2% of the total beds available across all hospital trusts included in the study.
Multilevel models were adjusted for trust size and affiliation as a teaching hospital, with trust included as a random effect to adjust for clustering. We considered a range of potential models, with model selection based on theoretical reasoning and model fit ). We report exponentiated coefficients with 95% CIs for all estimates obtained from the models.
The best performing model was a negative binomial random effects model that included all eight clinical staff groups and hospital characteristics . To explore variations between and within hospital trusts, we also constructed a within–between random effects model, with trusts as random effect, and staffing and hospital characteristics as fixed effects.
We performed data linkage, cleaning, coding and analyses in R statistical software, V.4.0.2 , using the lme4 and glmer packages , and the plm package (.2.4–3, Croissant
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