A comparison of in-hospital mortality risk conferred by high hospital occupancy, differences in nurse staffing levels, weekend admission, and seasonal influenza.
BACKGROUND
Hospital occupancy, nurse staffing levels, weekend admission, and seasonal influenza have all been shown to be associated with in-hospital mortality. Yet, no study has simultaneously compared the strength of associations of these 4 factors with in-hospital mortality.


OBJECTIVE
To compare the risk of in-hospital mortality conferred by high hospital occupancy on admission, increased nurse staffing levels, weekend admission, and seasonal influenza.


METHODS
Retrospective cohort study of 166,920 patients admitted to 39 Michigan hospitals between 2003 and 2006. Participants were adults, age > or = 65 years, admitted through the emergency department with 6 common discharge diagnoses (acute myocardial infarction, congestive heart failure, stroke, pneumonia, hip fracture, gastrointestinal bleeding). We used logistic regression to compare the differences in the predicted probability of death conferred by each of the 4 factors, controlling for patient age, gender, discharge diagnosis, and comorbid conditions.


RESULTS
Each of the 4 factors had a statistically significant, independent association with in-hospital mortality. Seasonal influenza conferred the greatest increase in absolute risk of in-hospital mortality (0.5 percentage points; 95% CI, 0.23-0.76), followed by weekend admission (0.32, 0.11-0.54), and high hospital occupancy on admission (0.24, 0.06-0.43). Increased nurse staffing levels decreased the absolute risk of mortality by 0.25 percentage points (0.04-0.48) for each additional full-time equivalent nurse per patient-day.


CONCLUSIONS
Hospital occupancy, nurse staffing levels, weekend admission, and seasonal influenza all appear to be independently associated with in-hospital mortality, but to varying degrees in the current sample. These findings may guide hospital administrators as they consider factors that influence weekly and seasonal patient flow and capacity, as well as staffing.
Schilling
Peter L
PL
Robert Wood Johnson Clinical Scholars Program, University of Michigan, Ann Arbor, MI 48109, USA. petschil@med.umich.edu
Campbell
Darrell A
DA
Englesbe
Michael J
MJ
Davis
Matthew M
MM
D000368:Aged; D000369:Aged, 80 and over; D001509:Bed Occupancy / Q000706:statistics & numerical data*; D015331:Cohort Studies; D015897:Comorbidity; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006739:Hospital Administration / Q000706:statistics & numerical data; D017052:Hospital Mortality; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D016015:Logistic Models; D008297:Male; D009741:Nursing Staff, Hospital / Q000458:organization & administration / Q000706:statistics & numerical data*; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D012621:Seasons; D012959:Socioeconomic Factors; D013997:Time Factors