Carole A. Estabrooks, RN, PhD William K. Midodzi, MSc Greta G. Cummings, RN, PhD(c)

1. Hierarchical analysis of the impact of hospital characteristics on mortality in Alberta hospitals Carole A. Estabrooks, RN, PhD William K. Midodzi, MSc Greta G. Cummings, RN, PhD(c) Kathryn L. Ricker, MSc Phyllis Giovannetti, RN, ScD Sigma Theta Tau International November, 2003

2. Financial Support Project Funding: • Alberta Heritage Foundation for Medical Research Career support: • Canadian Institutes of Health Research (CIHR) • Alberta Heritage Foundation for Medical Research

3. i n t r o d u c t i o n International Study of Hospital Outcomes Purpose To determine the effects of the organization and nurse staffing of hospitals on patient and nurse outcomes.

4. Motivation for Research Agenda • Widespread hospital restructuring and work redesign • Changing hospital staffing patterns • Absence of empirical evidence of these changes on outcomes

5. International samples sample HospitalsNurses Alberta 109 6,526 British Columbia 97 2,838 England 32 5,006 Germany 30 4,000 Ontario 209 8,778 Scotland 7 5,238 U.S. (PA) 210 14,145

6. Alberta sample HospitalsNurses 109 6,526 49 4,799 Criteria: 5 nurses & at least 20 beds

7. Sample Characteristics Sample (49 hospital) Population (109 hospital) Regular (FT/PT) 35.1/58.9 34.2/60.1 Casual 19.0 18.7 Female 97.2 97.5 Male 2.5 2.5 Age, yrs 40.4 40.9 Hours /wk 0-30 50.9 52.0 30 or more 46.5 45.5 Shift 8 Hrs 52.6 55.6 12 Hrs 35.8 33.8 Mixed 7.4 6.8 Diploma (RN) 77.2 77.4 Baccalaureate 22.5 22.1

8. Data Sources data sources • Nurse survey • Administrative data • Alberta CIHI Hospital Inpatient Database • Alberta Health Care Insurance Plan Registry • Characteristics of Alberta acute care hospitals

9. The Alberta Nurse Survey • Census of all staff nurses in hospitals (N=12,345) • Useable returns 6526 (52.8%) A: Employment Characteristics B: Nursing Work Index (NWI) C:Maslach Burnout Inventory(MBI) D: Job characteristics E:Last shift F: Demographics G: Site specific questions

10. The Model Organization Nurse Patient

11. 30-day mortality model Ability to develop relationships/Continuity of care Nursing training and skill variables Quality of work environment • Patient's characteristics • Age • Sex • Co morbidity factors • Complication • Chronicity 30-day mortality Other unknown determinants at the patient and hospital levels • Institutional factors • Bed size • Teaching hospital status • Hospital location • Hi-technology facility

12. Conceptual Basis for Hierarchical Modeling Z Level 2 (Hospital Level) Level 1 (Patient Level) X Y X = Individual patient characteristics (age, sex, admission diagnoses, comorbidity factors, in-hospital complications, etc. ) Z = Hospital characteristics (bed size, location, teaching status, nursing and physician factors, staffing, etc.) Y =The probability (or risk) of dying within 30 days of admission to hospital

13. Nursing variables analyzed at the hospital level Nursing training and skill variables • Nurse education level (% baccalaureate degree) • Skill mix, % Ability to develop a relationship with patients • Job status: casual or temporary staffs • Perception of quality care • Staffing, patients per nurse • Patients’ care needs unattended • Non-nursing activities performed by nurses Quality of work environment • Nurse job satisfaction • Support for non-floating policy • Nurse autonomy • Nurse-physician relationships • Emotional abuse

14. Inter-hospital variation in risk adjusted 30-day mortality High mortality 16 hospitals Low mortality 16 hospital Average mortality 17 hospitals Average co-morbidity score = 0.0752 Average co-morbidity score = 0.0725 Average co-morbidity score = 0.0726

15. Nursing characteristics of study hospitals

16. The Linear Hierarchical Models(Outcome:Risk adjusted 30-day mortality) • Model 1 (Patient Level): Controlling for patient factors • Age, years • Sex (Male/Female)

17. The Linear Hierarchical Models(Outcome: Risk adjusted 30-day mortality) • Model 1 (Patient Level): Controlling for patient factors • Age, years • Sex (Male/Female) • Model 2 (Hospital level): Institutional characteristics • Bed size ( <50, 51-150, >150) • Teaching status (>1 resident per 4 bed, <=1 residence per 4 bed, none) • Location of hospitals (small cities: pop. <=50K; large cities pop. >50K)

18. The Linear Hierarchical Models(Outcome: Risk adjusted 30-day mortality) • Model 1 (Patient Level): Controlling for patient factors • Age, years • Gender (Male/Female) • Model 2 (Hospital level): Instructional characteristics • Bed size • Teaching status Location of hospitals • Model 3 (Hospital Level): Nursing related hospital characteristics • Nurse education level • Skill mix • Job status: casual or temporary • Perception of quality care • Staffing ratio last shift • Patients’ care needs unattended • Non-nursing activities performed by nurses • Job satisfaction • Support for non-floating policy • Nurse autonomy • Nurse-physician relationships • Emotional abuse

19. Effect of nursing related hospital characteristics‡ Constant = - 5.19 - 0.50 x Nurse education level - 0.27 x Skill Mix (RN to total nurse staffs) +1.01 x Casual or temporary staffs - 0.14 x Perception of quality of care +0.01 x Staffing, pts. Per nurse ratio +0.08 x Patients care needs unattended +0.04 x Non-nursing activity performed - 0.12 x Job satisfaction problems - 0.06 x Support for non-floating policy - 0.22 x Nurse autonomy - 0.11 x Nurse-physician relationship +0.17 x Emotional abuse Variance in 30-day mortality among hospitals explained independently by nursing-derived factors = 33.1% ‡Model adjusted for pts co-morbidity factors, demographic variables and institutional factors

20. Percent of inter-hospital variation in30-day mortality explained by each factors Unknown determinants Patient’s co-morbidity factors Nursing-derived variables Patient’s demographic variables Institutional factors

21. Limitations • Administrative data • Aggregation • Generalizability

22. Summary Lower patient mortality across hospitals was predicted in our models by: • higher nurse education levels • work satisfaction • quality of care • support for non-floating policies • nurse autonomy • better nurse-physician relationships • richer skill mix of nursing

23. Summary Patients from hospitals with higher scores on the following had significantly greater risk of dying within 30-days of admission: • Higher percentage of casual employment • Higher numbers of unmet patient care needs • More non-nursing tasks completed • Higher levels of reported emotional abuse • Higher patient-to-nurse ratios

24. Implications