Table 1

Results of the fixed effects multivariate regression

Effect on the proportion of patients waiting over 4 hours (in percentage points change)Model (a)95% CIsModel (b)95% CIs
Hospital factorsBed occupancy:
 85%0(0.000 to 0.000)
 86%0.012(−0.001 to 0.025)
 87%0.009(−0.002 to 0.020)
 88%0.027***(0.015 to 0.038)
 89%0.023***(0.012 to 0.035)
 90%0.035***(0.022 to 0.047)
 91%0.035***(0.023 to 0.047)
 92%0.039***(0.026 to 0.052)
 93%0.043***(0.029 to 0.056)
 94%0.049***(0.036 to 0.063)
 95%0.052***(0.038 to 0.066)
 96%0.060***(0.046 to 0.074)
 97%0.062***(0.047 to 0.077)
 98%0.071***(0.056 to 0.086)
 99%0.084***(0.068 to 0.100)
 100%0.093***(0.075 to 0.111)
Admissions:discharges ratio0.037***(0.017 to 0.057)
L1.admissions:discharges ratio0.047***(0.034 to 0.059)
L2.admissions:discharges ratio0.054***(0.042 to 0.067)
Daily admissions ratio0.081***(0.063 to 0.100)
Total admissions0.000(−0.000 to 0.000)
Senior doctors−0.047(−0.270 to 0.177)−0.038(−0.272 to 0.197)
Inpatient typesLong-stay patients0.069*(0.008 to 0.130)0.128***(0.064 to 0.192)
Influenza0.384(−0.263 to 1.031)0.485(−0.125 to 1.095)
Control variablesYesYes
Observations11 79011 544
R20.2630.230
  • We analysed two adaptations of the model: model (a), which included indicator variables indicating the bed occupancy level in percentage points and the daily admissions ratio; and model (b), which omitted bed occupancy variables and instead included continuous variables measuring the patient flows that determined occupancy: total daily admissions and admissions to discharges ratio. Other control variables are the same in both models. 95% confidence intervals in brackets. Clustered standard errors. The results for the bed occupancy intervals below and equal to 85% are grouped in the 85% category—only 6% of observations of bed occupancy were below that level and the results of the regression on those intervals were not significant. Full tables can be found in the supplementary online material.

  • *p < 0.05, **p < 0.01, ***p < 0.001.