Jama

Confidential. Do not distribute. Pre-embargo material.ORIGINAL CONTRIBUTION

Hospital Nurse Staffing andPatient Mortality, Nurse Burnout,and Job DissatisfactionLinda H. Aiken, PhD, RNSean P. Clarke, PhD, RNDouglas M. Sloane, PhDJulie Sochalski, PhD, RNJeffrey H. Silber, MD, PhD

THE PAST DECADE HAS BEEN A TUR-bulent time for US hospitals andpracticing nurses. News mediahave trumpeted urgent con-

cerns about hospital understaffing anda growing hospital nurse shortage.1-3

Nurses nationwide consistently reportthat hospital nurse staffing levels are in-adequate to provide safe and effectivecare.4-6 Physicians agree, citing inad-equate nurse staffing as a major impedi-ment to the provision of high-qualityhospital care.7 The shortage of hospitalnurses may be linked to unrealistic nurseworkloads.8 Forty percent of hospitalnurses have burnout levels that exceedthe norms for health care workers.4 Jobdissatisfaction among hospital nurses is4 times greater than the average for allUS workers, and 1 in 5 hospital nursesreport that they intend to leave their cur-rent jobs within a year.4

In 1999, California passed legisla-tion mandating patient-to-nurse ra-tios for its hospitals, which goes intoeffect in July 2003. The California leg-islation was motivated by an increas-ing hospital nursing shortage and theperception that lower nurse retentionin hospital practice was related to bur-

densome workloads and high levels ofjob-related burnout and job dissatis-faction. Stakeholder groups advo-cated widely divergent minimum ra-tios. On medical and surgical units,recommended ratios ranged from 3to 10 patients for each nurse.9-11 Inearly 2002, California’s governor an-nounced that hospitals must have atleast 1 licensed nurse for every 6 medi-cal and surgical patients by July 2003,

Author Affiliations: Center for Health Outcomes andPolicy Research, School of Nursing (Drs Aiken, Clarke,Sloane, and Sochalski), Leonard Davis Institute of HealthEconomics (Drs Aiken, Clarke, Sochalski, and Silber), De-partment of Sociology (Dr Aiken), Population StudiesCenter (Drs Aiken, Sloane, and Sochalski), and Depart-ments of Pediatrics and Anesthesia, School of Medi-cine (Dr Silber), University of Pennsylvania, Philadel-phia; and Center for Outcomes Research, Children’sHospital of Philadelphia, Philadelphia, Pa (Dr Silber).Corresponding Author and Reprints: Linda H. Aiken,PhD, RN, Center for Health Outcomes and Policy Re-search, University of Pennsylvania, 420 Guardian Dr,Philadelphia, PA 19104-6096 (e-mail: [email protected]).

Context The worsening hospital nurse shortage and recent California legislationmandating minimum hospital patient-to-nurse ratios demand an understanding ofhow nurse staffing levels affect patient outcomes and nurse retention in hospitalpractice.

Objective To determine the association between the patient-to-nurse ratio and pa-tient mortality, failure-to-rescue (deaths following complications) among surgical pa-tients, and factors related to nurse retention.

Design, Setting, and Participants Cross-sectional analyses of linked data from10184 staff nurses surveyed, 232342 general, orthopedic, and vascular surgerypatients discharged from the hospital between April 1, 1998, and November 30,1999, and administrative data from 168 nonfederal adult general hospitals in Penn-sylvania.

Main Outcome Measures Risk-adjusted patient mortality and failure-to-rescuewithin 30 days of admission, and nurse-reported job dissatisfaction and job-relatedburnout.

Results After adjusting for patient and hospital characteristics (size, teaching status,and technology), each additional patient per nurse was associated with a 7% (oddsratio [OR], 1.07; 95% confidence interval [CI], 1.03-1.12) increase in the likelihoodof dying within 30 days of admission and a 7% (OR, 1.07; 95% CI, 1.02-1.11) in-crease in the odds of failure-to-rescue. After adjusting for nurse and hospital charac-teristics, each additional patient per nurse was associated with a 23% (OR, 1.23; 95%CI, 1.13-1.34) increase in the odds of burnout and a 15% (OR, 1.15; 95% CI, 1.07-1.25) increase in the odds of job dissatisfaction.

Conclusions In hospitals with high patient-to-nurse ratios, surgical patients expe-rience higher risk-adjusted 30-day mortality and failure-to-rescue rates, and nursesare more likely to experience burnout and job dissatisfaction.JAMA. 2002;288:1987-1993 www.jama.com

For editorial comment see p 2040.

©2002 American Medical Association. All rights reserved. (Reprinted) JAMA, October 23/30, 2002—Vol 288, No. 16 1987

Confidential. Do not distribute. Pre-embargo material.

a ratio that will move to 1 to 5 whenthe mandates are fully implemented.12

This study reports on findings from acomprehensive study of 168 hospitalsand clarifies the impact of nurse staff-ing levels on patient outcomes and fac-tors that influence nurse retention.13 Spe-cifically, we examined whether risk-adjusted surgical mortality and rates offailure-to-rescue (deaths in surgical pa-tients who develop serious complica-tions) are lower in hospitals where nursescarry smaller patient loads. In addition,we ascertained the extent to which morefavorable patient-to-nurse ratios are as-sociated with lower burnout and higherjob satisfaction among registered nurses.We also estimated excess surgical deathsassociated with the different nurse staff-ing ratios vigorously debated in Califor-nia. Finally, we estimated the impact ofnurse staffing levels proposed in Cali-fornia on nurse burnout and dissatisfac-tion, 2 precursors of turnover.13 Our find-ings offer insights into how moregenerous registered nurse staffing mightaffect patient outcomes and inform cur-rent debates in many states regarding themerits of legislative actions to influencestaffing levels.

METHODSPatients, Data Sources,and Variables

Our study combines information abouthospital staffing and organization ob-tained from nurse surveys with patientoutcomes derived from hospital dis-charge abstracts and hospital character-istics drawn from administrative data-bases.14 The study protocol for linkinganonymized nurse data and handling de-nominalized patient data was ap-proved by the institutional review boardof the University of Pennsylvania.

Hospitals. Data were collected on all210 adult general hospitals in Pennsyl-vania. Information about hospital char-acteristics was derived from the 1999American Hospital Association (AHA)Annual Survey and the 1999 Pennsyl-vania Department of Health HospitalSurvey.15,16 Ultimately, 168 of the 210acute care hospitals had discharge datafor surgical patients in the targeted Di-

agnosis Related Groups (DRGs) dur-ing the study period, as well AHA data,and survey data from 10 or more staffnurses. Six of the excluded hospitalswere Veterans Affairs hospitals, whichdo not report discharge data to the state.Twenty-six hospitals were excluded be-cause their administrative or patient out-comes data could not be matched to oursurveys because of missing variables, pri-marily because they reported their char-acteristics or patient data as aggregatemultihospital entities. In 10 additionalsmall hospitals, the majority of whichhad fewer than 50 beds, fewer than 10nurses responded to the survey.

A nurse staffing measure was calcu-lated as the mean patient load across allstaff registered nurses who reportedhaving responsibility for at least 1 butfewer than 20 patients on the last shiftthey worked, regardless of the spe-cialty or shift (day, evening, night)worked. This measure of staffing issuperior to those derived from admin-istrative databases, which generallyinclude registered nurse positions thatdo not involve inpatient acute care atthe bedside. Staffing was measuredacross entire hospitals because there isno evidence that specialty-specific staff-ing offers advantages in the study ofpatient outcome17 and to reflect the factthat patients often receive nursing carein multiple specialty areas of a hospi-tal. Direct measurement also avoidedproblems with missing data commonto the AHA’s Annual Survey of hospi-tals,which imputedstaffingdata in1999for 20% of Pennsylvania hospitals.

Three hospital characteristics wereused as control variables: size, teachingstatus, and technology. Hospitals weregrouped into 3 size categories: small(�100 hospital beds), medium (101-250 hospital beds), and large (�251 hos-pital beds). Teaching status was mea-sured by the ratio of resident physiciansand fellows to hospital beds, which hasbeen suggested as superior to univer-sity affiliations and association member-ships as an indicator of the intensity ofteaching activity.18 Hospitals with nopostgraduate trainees (nonteaching)were contrasted with those that had 1:4

or smaller trainee:bed ratios (minorteaching hospitals) and those with ra-tios that were higher than 1:4 (majorteaching hospitals). Finally, hospitalswith facilities for open heart surgeryand/or major transplants were classi-fied as high-technology hospitals andcontrasted with other hospitals.19

Nurses and Nurse Outcomes. Sur-veys were mailed in the spring of 1999to a 50% random sample of registerednurses who were on the PennsylvaniaBoard of Nursing rolls and resided in thestate. The response rate was 52%, whichcompares favorably with rates seen inother voluntary surveys of health pro-fessionals.20 Roughly one third of thenurses who responded worked in hos-pitals and included the sample of 10184nurses described here. No special re-cruiting methods or inducements wereused. Demographic characteristics of therespondents matched the profile forPennsylvania nurses in the NationalSample Survey of Registered Nurses.21

Nurses employed in hospitals were askedto use a list to identify the hospital inwhich they worked, and then were que-ried about their demographic character-istics, work history, workload, job sat-isfaction, and feelings of job-relatedburnout. Questionnaires were returnedby nurses employed at each of the 210Pennsylvania hospitals providing adultacute care. To obtain reliable hospital-level estimates of nurse staffing (the ra-tio of patients to nurses in each hospi-tal), attention was restricted to registerednurses holding staff nurse positions in-volving direct patient care and to hos-pitals from which at least 10 such nursesreturned questionnaires. In 80% of the168 hospitals in the final sample, 20 ormore nurses provided responses to ourquestionnaire. There were more than 50nurse respondents from half of the hos-pitals. We examined 2 nurse job out-comes in relation to staffing: job satis-faction(ratedona4-point scale fromverydissatisfied to very satisfied) and burn-out (measured with the Emotional Ex-haustion scale of the Maslach BurnoutInventory, a standardized tool).22,23

Patients and Patient Outcomes. Dis-charge abstracts representing all admis-

NURSE STAFFING AND HOSPITAL OUTCOMES

1988 JAMA, October 23/30, 2002—Vol 288, No. 16 (Reprinted) ©2002 American Medical Association. All rights reserved.


sions to nonfederal hospitals in Penn-sylvaniafrom1998to1999wereobtainedfrom the Pennsylvania Health Care CostContainment Council. These dischargeabstracts were merged with Pennsylva-nia vital statistics records to identifypatients who died within 30 days of hos-pital admission to control for timing ofdischarge as a possible source of varia-tion in hospital outcomes. We exam-ined outcomes for 232342 patientsbetween the ages of 20 and 85 years whounderwent general surgical, orthope-dic, or vascular procedures in the 168hospitals from April 1, 1998, to Novem-ber 30, 1999. Surgical discharges wereselected for study because of the avail-ability of well-validated risk adjustmentmodels.24-29 The number of patients dis-charged from the study hospitals rangedfrom 75 to 7746. Only the first hospitaladmission for any of the DRGs listed inthe BOX for any patient during the studyperiod was included in the analyses.

In addition to 30-day mortality, weexamined failure-to-rescue (deathswithin 30 days of admission amongpatients who experienced complica-tions).24-29 Complications were identi-fied by scanning discharge abstracts forInternational Classification of Diseases,Ninth Revision, Clinical Modification(ICD-9-CM) codes in the secondary di-agnosis and procedure fields that weresuggestive of 39 different clinical events.Distinguishing complications from pre-viously existing comorbidities in-volved the use of rules developed by ex-pert consensus and previous empiricalwork, as well as examination of dis-charge records for each patient’s hospi-talizations 90 days before the surgery ofinterest for overlap in secondary diag-nosis codes.27-29 Examples of complica-tions included aspiration pneumoniaand hypotension/shock. Patients whodied postoperatively were assumed tohave developed a complication even ifno complication codes were identifiedin their discharge abstracts.

Risk adjustment of mortality and fail-ure-to-rescue for patient characteris-tics and comorbidities was accom-plished by using 133 variables, includingage, sex, surgery types, and dummy vari-

ables indicating the presence of chronicpreexisting health conditions reflectedin the ICD-9-CM codes in the dis-charge abstracts (eg, diabetes melli-tus), as well as a series of interactionterms. The final set of control variableswas determined by a selection processthat paralleled an approach used and re-ported previously.27-29 The C statistic(area under the receiver operating char-acteristic curve) for the mortality risk ad-justment model was 0.89.30

Data AnalysisDescriptive data show how patients andnurses in our sample were distributedacross the various categories of hospi-tals defined by staffing levels and othercharacteristics. Logistic regression mod-els were used to estimate the effects ofstaffing on the nurse outcomes (job dis-satisfaction and burnout) and 2 patientoutcomes (mortality and failure-to-rescue). We computed the odds ofnurses being moderately or very dissat-isfied with their current positions andreporting a level of emotional exhaus-tion (burnout) above published normsfor medical workers and of patients ex-periencing mortality and failure-to-rescue under different levels of regis-tered nurse staffing, before and aftercontrol for individual characteristics andhospital variables. For nurse out-comes, we adjusted for sex, years of ex-perience in nursing, education (bacca-laureate degree or above vs diploma orassociate degree as highest credential innursing), and nursing specialty. Foranalyses of patient outcomes, we con-trolled for the variables in our risk ad-justment model, specifically, demo-graphic characteristics of patients, nature

of the hospital admission, comorbidi-ties, and relevant interaction terms. Foranalyses of both patient and nurse out-comes, we adjusted for hospital size,teaching status, and technology.

All logistic regression models were es-timated by using Huber-White (ro-bust) procedures to account for the clus-tering of patients within hospitals andadjust the SEs of the parameter esti-mates appropriately.31,32 Model calibra-tion was assessed with the Hosmer-Lemeshow statistic.33 We used directstandardization to illustrate the magni-tude of the effect of staffing by estimat-ing the difference in the numbers ofdeaths and episodes of failure-to-rescue under different staffing sce-narios. Using all patients in the study andusing the final fully-adjusted model, weestimated the probability of death andfailure-to-rescue for each patient un-der various patient-to-nurse ratios (ie,4, 6, and 8 patients per nurse) with allother patient characteristics un-changed. We then calculated the differ-ences in total deaths under the differ-ent scenarios.34 Confidence intervals(CIs) for these direct standardization es-timates were derived with the � methoddescribed by Agresti.35 All analyses wereperformed using STATA version 7.0(STATA Corp, College Station, Tex), andP<.05 was considered statistically sig-nificant in all analyses.

RESULTSCharacteristics of Hospitals,Nurses, and Patients

Distributions of hospitals with variouscharacteristics, distributions of nursessurveyed, and patients whose out-comes were studied are shown in

Box. Surgical Patient Diagnosis Related Groups Includedin the Analyses of Mortality and Failure-to-Rescue

General Surgery146-155, 157-162, 164-167, 170, 171, 191-201, 257-268, 285-293, 493, and 494

Orthopedic Surgery209-211, 213, 216-219, 223-234, 471, 491, and 496-503

Vascular Surgery110-114, 119, and 120




TABLE 1. Fifty percent of the hospitalshad patient-to-nurse ratios that were 5:1or lower, and those hospitals dis-charged 65.9% of the patients in thestudy and employed 64.4% of the nurseswe surveyed. Hospitals with more than250 beds accounted for a disproportion-ate share of both patients and nurses(45.5% and 43.4%, respectively). Al-though high-technology hospitals ac-counted for only 28.0% of the institu-tions studied, more than half (55.3%)of the patients discharged and 53.8%of nurses surveyed were from high-technology hospitals. A majority ofthe patients studied and nurses sur-

veyed were drawn from the 61 hospi-tals (36.3%) that reported postgradu-ate medical trainees in 1999.

As shown in TABLE 2, 94.1% of thenurses were women and 39.6% held abaccalaureate degree or higher. Themean (SD) work experience in nurs-ing was 13.8 years (9.8). Thirty-one per-cent of the nurses in the sample workedon medical and surgical general units,while 19.6% and 9.8% worked in in-tensive care and perioperative set-tings, respectively. Forty-three per-cent of the nurses had high burnoutscores and a similar proportion weredissatisfied with their current jobs.

Of the232342patients studied,53813(23.2%) experienced a major complica-tion not present on admission and 4535(2.0%) died within 30 days of admis-sion. The death rate among patients withcomplications was 8.4%. The surgicalcase types and clinical characteristics ofthe patient cohort are shown in TABLE 3.Slightly more than half of patients(51.2%) were classified in an orthope-dic surgery DRG, with the next largestgroup of patients (36.4%) undergoing di-gestive tract and hepatobiliary surger-ies. Chronic medical conditions, with theexception of hypertension, were rela-tively uncommon among these pa-tients. Patients who experienced com-

plications and were included in ouranalyses of failure-to-rescue were simi-lar to the broader group of patients in ourmortality analyses with respect to theircomorbidities, but orthopedic surgerypatients were less prominently repre-sented among patients with complica-tions than in the overall sample.

Staffing and Job Satisfactionand BurnoutHigheremotionalexhaustionandgreaterjobdissatisfactioninnurseswerestronglyandsignificantlyassociatedwithpatient-to-nurse ratios. TABLE 4 shows oddsratios (ORs) indicating how much morelikely nurses in hospitals with higherpatient-to-nurse ratios were to exhibitburnout scores above published normsandtobedissatisfiedwiththeir jobs.Con-trolling for nurse and hospital charac-teristics resulted in a slight increase inthese ratios, which in both cases indi-catedapronouncedeffectof staffing.Thefinal adjusted ORs indicated that anincrease of 1 patient per nurse to a hos-pital’sstaffinglevel increasedburnoutandjobdissatisfactionbyfactorsof1.23(95%CI, 1.13-1.34) and 1.15 (95% CI, 1.07-1.25), respectively, or by 23% and 15%.This implies thatnurses inhospitalswith8:1patient-to-nurse ratioswouldbe2.29times as likely as nurses with 4:1 patient-to-nurse ratios to show high emotionalexhaustion (ie, 1.23 to the 4th power for4additionalpatientspernurse=2.29)and1.75 timesas likely tobedissatisfiedwiththeir jobs (ie, 1.15 to the 4th power for4 additional patients per nurse=1.75).Our data further indicate that, although43% of nurses who report high burnoutandaredissatisfiedwith their jobs intendto leave their current job within the next12 months, only 11% of the nurses whoare not burned out and who remain sat-isfied with their jobs intend to leave.

Staffing and Patient Mortalityand Failure-to-RescueAmong the surgical patients studied,there was a pronounced effect of nursestaffing on both mortality and mortal-ity following complications. Table 4 alsoshows the relationship between nursestaffing and patient mortality and failure-

Table 1. Study Hospitals, Surgical Patients Studied, and Nurse Respondents in Hospitals*

Characteristic

No. (%)

Hospitals(N = 168)

Patients(N = 232 342)

Nurses(N = 10 184)

Staffing, patients per nurse�4 20 (11.9) 41 414 (17.8) 1741 (17.1)5 64 (38.1) 111 752 (48.1) 4818 (47.3)6 41 (24.4) 48 120 (20.7) 2114 (20.8)7 29 (17.3) 21 360 (9.2) 1106 (10.9)�8 14 (8.3) 9696 (4.2) 405 (4.0)

Size, No. of beds�100 41 (24.4) 16 123 (6.9) 842 (8.3)101-250 95 (56.6) 110 510 (47.6) 4927 (48.4)�251 32 (19.1) 105 709 (45.5) 4415 (43.4)

TechnologyNot high 121 (72.0) 103 824 (44.7) 4706 (46.2)High 47 (28.0) 128 518 (55.3) 5478 (53.8)

Teaching statusNone 107 (63.7) 98 937 (42.6) 4553 (44.7)Minor 44 (26.2) 80 127 (34.5) 3435 (33.7)Major 17 (10.1) 53 278 (22.9) 2196 (21.6)

*Percentages may not add up to 100 because of rounding.

Table 2. Characteristics of Nurses(N = 10 184) in the Study Hospitals*

Characteristic No. (%)

Women 9425 (94.1)BSN degree or higher 3980 (39.6)Years worked as a nurse, mean (SD) 13.8 (9.8)Clinical specialty

Medical and surgical 3158 (31.0)Intensive care 1992 (19.6)Operating/recovery room 998 (9.8)Other 4026 (39.6)

High emotional exhaustion 3926 (43.2)Dissatisfied with current job 4162 (41.5)

*Sample size for individual characteristics varied be-cause of missing data. BSN indicates bachelor of sci-ence in nursing. High emotional exhaustion refers to lev-els of emotional exhaustion above the published “high”norm for medical workers.20 Dissatisfied with current jobcombines nurses who reported being either very dis-satisfied or a little dissatisfied.




to-rescue (mortality following compli-cations) when other factors were ig-nored, after patient characteristics werecontrolled, and after patient character-istics and other hospital characteristics(size, teaching status, and technology)were controlled. Although the ORs re-flecting the nurse staffing effect weresomewhat diminished by controlling forpatient and hospital characteristics, theyremained sizable and significant for bothmortality and failure-to-rescue (1.07;95% CI, 1.03-1.12 and 1.07; 95% CI,1.02-1.11, respectively). An OR of 1.07implies that the odds of patient mortal-ity increased by 7% for every additionalpatient in the average nurse’s workloadin the hospital and that the differencefrom 4 to 6 and from 4 to 8 patients pernursewouldbeaccompaniedby14%and31% increases in mortality, respectively(ie, 1.07 to the 2nd power=1.14 and 1.07to the 4th power=1.31).

These effects imply that, all else beingequal, substantial decreases in mortal-ity rates could result from increasing reg-istered nurse staffing, especially for pa-tients who develop complications. Directstandardization techniques were used topredict excess deaths in all patients andinpatientswithcomplications thatwouldbe expected if the patient-to-nurse ratiofor all patients in the study were at vari-ous levels that figure prominently in theCalifornia staffingmandatedebates. If thestaffing ratio in all hospitals was 6 pa-tients per nurse rather than 4 patients pernurse, we would expect 2.3 (95% CI, 1.1-3.5) additional deaths per 1000 pa-

tients and 8.7 (95% CI, 3.9-13.5) addi-tional deaths per 1000 patients withcomplications. If the staffing ratio in allhospitals was 8 patients per nurse rather

than 6 patients per nurse, we would ex-pect 2.6 (95% CI, 1.2-4.0) additionaldeaths per 1000 patients and 9.5 (95%CI, 3.8-15.2) additional deaths per 1000

Table 3. Characteristics of the Surgical Patients Included in Analyses of Mortality andFailure-to-Rescue*

Characteristic

No. (%)

All Patients(N = 232 342)

Patients WithComplications

(n = 53 813)

Men 101 624 (43.7) 25 619 (47.6)

Age, mean (SD) 59.3 (16.9) 64.2 (15.7)

Emergency admissions 63 355 (27.3) 21 541 (40.0)

Deaths within 30 days of admission 4535 (2.0) 4535 (8.4)

Major Diagnostic Categories (MDCs)General surgery

Diseases and disorders of thedigestive system (MDC 6)

54 919 (23.6) 19 002 (35.3)

Diseases and disorders of the hepatobiliarysystem (MDC 7)

29 660 (12.8) 6804 (12.6)

Diseases and disorders of the skin,subcutaneous tissue, and breast (MDC 9)

12 771 (5.5) 3010 (5.6)

Endocrine, nutritional, metabolic diseases,and disorders (MDC 10)

4853 (2.1) 1535 (2.9)

Orthopedic surgery

Diseases and disorders of the musculoskeletalsystem (MDC 8)

118 945 (51.2) 17 403 (32.3)

Vascular surgery

Diseases and disorders of the circulatorysystem (MDC 5)

11 194 (4.8) 6059 (11.3)

Medical history (comorbidities)Congestive heart failure 11 795 (5.1) 5735 (10.7)

Arrhythmia 3965 (1.7) 1765 (3.3)

Aortic stenosis 2248 (1.0) 848 (1.6)

Hypertension 79 827 (34.4) 20 648 (38.4)

Cancer 28 558 (12.3) 9074 (16.9)

Chronic obstructive pulmonary disease 19 819 (8.5) 7612 (14.2)

Diabetes mellitus (insulin and noninsulin dependent) 31 385 (13.5) 9597 (17.8)

Insulin-dependent diabetes mellitus 3607 (1.6) 1755 (3.3)

*Patients who died postoperatively were assumed to have developed a complication even if no complication codeswere identified in their discharge abstracts.

Table 4. Patient-to-Nurse Ratios With High Emotional Exhaustion and Job Dissatisfaction Among Staff Nurses and With Patient Mortality andFailure-to-Rescue*

Odds Ratio (95% Confidence Interval)

UnadjustedP

Value

Adjusted forNurse or PatientCharacteristics

PValue

Adjusted forNurse or Patient and

Hospital CharacteristicsP

Value

Nurse outcomesHigh emotional exhaustion 1.17 (1.10-1.26) �.001 1.17 (1.10-1.26) �.001 1.23 (1.13-1.34) �.001

Job dissatisfaction 1.11 (1.03-1.19) .004 1.12 (1.04-1.19) .001 1.15 (1.07-1.25) �.001

Patient outcomesMortality 1.14 (1.08-1.19) �.001 1.09 (1.04-1.13) �.001 1.07 (1.03-1.12) �.001

Failure-to-rescue 1.11 (1.06-1.17) .004 1.09 (1.04-1.13) .001 1.07 (1.02-1.11) �.001

*Odds ratios, indicating the risk associated with an increase of 1 patient per nurse, and confidence intervals were derived from robust logistic regression models that accounted forthe clustering (and lack of independence) of observations within hospitals. Nurse characteristics were adjusted for sex, experience (years worked as a nurse), type of degree, andtype of unit. Patient characteristics were adjusted for the patient’s Diagnosis Related Groups, comorbidities, and significant interactions between them. Hospital characteristicswere adjusted for high technology, teaching status, and size (number of beds).




patients with complications. Staffing hos-pitals uniformly at 8 vs 4 patients pernurse would be expected to entail 5.0(95% CI, 2.4-7.6) excess deaths per 1000patients and 18.2 (95% CI, 7.7-28.7) ex-cess deaths per 1000 complicated pa-tients. We were unable to estimate ex-cess deaths or failures associated with aratio of 10 patients per nurse (one of thelevels proposed in California) becausethere were so few hospitals in our samplestaffed at that level.

COMMENTRegistered nurses constitute an around-the-clock surveillance system in hospi-tals for early detection and prompt inter-vention when patients’ conditionsdeteriorate. The effectiveness of nursesurveillance is influenced by the num-berof registerednursesavailable toassesspatients on an ongoing basis. Thus, it isnot surprising that we found nurse staff-ing ratios to be important in explainingvariation in hospital mortality. Numer-ous studies have reported an associa-tion between more registered nurses andlower hospital mortality, but often as aby-product of analyses focusing directlyon some other aspect of hospitalresources such as ownership, teachingstatus, or anesthesiologist direc-tion.19,27,36-42 Therefore, a simple searchfor literature dealing with the relation-ship between nurse staffing and patientoutcomes yields only a fraction of thestudies that have relevant findings. Therelative inaccessibility of this evidencebase might account for the influentialAudit Commission in England conclud-ing recently that there isnoevidence thatmore favorable patient-to-nurse ratiosresult in better patient outcomes.43

Our results suggest that the Califor-nia hospital nurse staffing legislationrepresents a credible approach to re-ducing mortality and increasing nurseretention in hospital practice, if it canbe successfully implemented. More-over, our findings suggest that Califor-nia officials were wise to reject ratiosfavored by hospital stakeholder groupsof 10 patients to each nurse on medi-cal and surgical general units in favorof more generous staffing require-

ments of 5 to 6 patients per nurse. Ourresults do not directly indicate howmany nurses are needed to care for pa-tients or whether there is some maxi-mum ratio of patients per nurse abovewhich hospitals should not venture.Our major point is that there are de-tectable differences in risk-adjustedmortality and failure-to-rescue ratesacross hospitals with different regis-tered nurse staffing ratios.

In our sample of 168 Pennsylvaniahospitals in which the mean patient-to-nurse ratio ranged from 4:1 to 8:1, 4535of the 232342 surgical patients with theclinical characteristics we selected diedwithin 30 days of being admitted. Ourresults imply that had the patient-to-nurse ratio across all Pennsylvania hos-pitals been 4:1, possibly 4000 of these pa-tients may have died, and had it been 8:1,more than 5000 of them may have died.While this difference of 1000 deaths inPennsylvania hospitals across the 2 staff-ing scenarios is approximate, it repre-sents a conservative estimate of prevent-able deaths attributable to nurse staffingin the state. Our sample of patients rep-resents only about half of all surgicalcases in these hospitals, and other pa-tients admitted to these hospitals are atrisk of dying and similarly subject to theeffects of staffing. Moreover, in Califor-nia,which has nearly twice as many acutecare hospitals and discharges and anoverall inpatient mortality rate higherthan inour sample inPennsylvania (2.3%vs 2.0%), it would be reasonable to ex-pect that the difference of 4 fewer pa-tients per nurse might result in 2000 ormore preventable deaths throughout asimilar period.

Ourresults further indicate thatnursesin hospitals with the highest patient-to-nurse ratios are more than twice as likelyto experience job-related burnout andalmost twice as likely to be dissatisfiedwith their jobs compared with nurses inthe hospitals with the lowest ratios. Thiseffect of staffing on job satisfaction andburnout suggests that improvements innurse staffing in California hospitalsresulting from the new legislation couldbe accompanied by declines in nurseturnover. We found that burnout and

dissatisfaction predict nurses’ inten-tions to leave their current jobs withina year. Although we do not know howmany of the nurses who indicated inten-tions to leave their jobs actually did so,it seems reasonable to assume that the4-fold difference in intentions acrossthese 2 groups translated to at least asimilar difference in nurse resigna-tions. If recently published estimates ofthe costs of replacing a hospital medi-cal and surgical general unit and a spe-cialty nurse of $42000 and $64000,respectively, arecorrect, improvingstaff-ing may not only save patient lives anddecrease nurse turnover but also reducehospital costs.44

Additional analyses indicate that ourconclusions about the effects of staff-ing and the size of these effects are simi-lar under a variety of specifications. Weallowed the effect of nurse staffing tobe nonlinear (using a quadratic term)and vary in size across staffing levels (us-ing dummy variables and interactionterms) and found no evidence in thissample of hospitals that additional reg-isterednurse staffinghasdifferent effectsat differing staffing levels. Limiting ouranalyses to general and orthopedic sur-gery patients and eliminating vascularsurgery patients (who have higher mor-tality and complication rates) did notaffect our conclusions and effect-sizeestimates. Also, our findings were notchanged by restricting attention to inpa-tient deaths vs deaths within 30 daysof admission. Results were unaffectedby restricting analyses to patients whowere discharged after our staffing mea-sures were obtained, rather than to thepatients who were discharged from 9months before to 9 months followingthe nurse surveys that produced ourstaffing measures. They were alsounchanged by restricting the sample ofnurses from which we derived our staff-ing measures to medical and surgicalnurses, as opposed to all staff nurses.Finally, they were neither altered byadjusting for patient-to-licensed prac-tical nurse ratios and patient-to-unlicensed assistive personnel ratios(neither of which were related to patientoutcomes) nor affected by excluding the




hospitals in our sample with smallernumbers of patients or nurses.

One limitation of this study is the po-tential for response bias, given a 52% re-sponse rate. We find no evidence that thenurses in our sample were dispropor-tionately dissatisfied with their work rela-tive to Pennsylvania staff nurses from theNational Sample Survey of RegisteredNurses (a national probability-basedsample surveyperformed in2000).21 Fur-thermore, with respect to demographiccharacteristics (sex, age, and educa-tion) included in both surveys, oursample of nurses also closely resemblesthose participating in the NationalSample Survey of Registered Nurses. Weare confident that these results are notspecific to this particular sample ofnurses. Ultimately, longitudinal data setswill be needed to exclude the possibil-ity that low hospital nurse staffing is theconsequence, rather than the cause, ofpoor patient and nurse outcomes.

Our findings have important impli-cations for 2 pressing issues: patientsafety and the hospital nurse shortage.Our results document sizable and sig-nificant effects of registered nurse staff-ing on preventable deaths. The associa-tion of nurse staffing levels with therescue of patients with life-threateningconditions suggests that nurses contrib-ute importantly to surveillance, early de-tection, and timely interventions thatsave lives. The benefits of improved reg-istered nurse staffing also extend to thelarger numbers of hospitalized patientswho are not at high risk for mortality butnevertheless are vulnerable to a widerange of unfavorable outcomes. Improv-ing nurse staffing levels may reducealarming turnover rates in hospitals byreducing burnout and job dissatisfac-tion, major precursors of job resigna-tion. When taken together, the im-pacts of staffing on patient and nurseoutcomes suggest that by investing inregistered nurse staffing, hospitals mayavert both preventable mortality and lownurse retention in hospital practice.

Author Contributions: Study concept and design:Aiken, Clarke, Sloane, Sochalski, Silber.Acquisition of data: Aiken, Clarke, Sochalski, Silber.Analysis and interpretation of data: Aiken, Clarke,Sloane, Silber.

Drafting of the manuscript: Aiken, Clarke, Sloane,Silber.Critical revision of the manuscript for important intel-lectual content: Aiken, Clarke, Sloane, Sochalski, Silber.Statistical expertise: Clarke, Sloane, Silber.Obtained funding: Aiken, Sloane, Sochalski.Administrative, technical, or material support: Aiken,Clarke, Sochalski, Silber.Study supervision: Aiken, Clarke, Silber.Funding/Support: This study was supported by grantR01 NR04513 from the National Institute of NursingResearch, National Institutes of Health.Acknowledgment: We thank Paul Allison, PhD, fromthe University of Pennsylvania for statistical consul-tation, and Xuemei Zhang, MS, Wei Chen, MS, andOrit Even-Shoshan, MS, from the Center for Out-comes Research at the Children’s Hospital of Phila-delphia for their assistance.

REFERENCES1. Stolberg SG. Patient deaths tied to lack of nurses.New York Times. August 8, 2002:A18.2. Parker-Pope T. How to lessen impact of nursingshortage on your hospital stay. Wall Street Journal.March 2, 2001:B1.3. Trafford A. Second opinion: less care for patients.Washington Post. August 20, 2002:HE01.4. Aiken LH, Clarke SP, Sloane DM, et al. Nurses’ re-ports on hospital care in five countries. Health Aff(Millwood). 2001;20:43-53.5. Henry J. Kaiser Family Foundation. Survey of phy-sicians and nurses. Available at: http://www.kff.org/content/1999/1503. Accessed March 22, 2002.6. Shindul-Rothschild J, Berry D, Long-Middleton E.Where have all the nurses gone? final results of ourPatient Care Survey. Am J Nurs. 1996;96:25-39.7. Commonwealth Fund. Doctors in five countries seedecline in health care quality. Commonwealth FundQuarterly. 2000;6:1-4.8. Joint Commission on Accreditation of HealthcareOrganizations. Health care at the crossroads: strate-gies for addressing the evolving nursing crisis, 2002.Available at: http://www.jcaho.org/news+room/news + release + archives/health + care + at + the+crossroads.pdf. Accessed September 17, 2002.9. Spetz J. What should we expect from California’sminimum nurse staffing legislation. J Nurs Adm. 2001;31:132-140.10. Seago JA. The California experiment: alterna-tives for minimum nurse-to-patient ratios. J Nurs Adm.2002;32:48-58.11. Friedman L. Nurse ratios are still too high. San Fran-cisco Chronicle. January 30, 2002:A19.12. Governor Gray Davis announces proposed nurse-to-patient ratios [press release]. Sacramento, Calif: Of-fice of the Governor; January 22, 2002.13. Lake ET. Advances in understanding and predict-ing nurse turnover. Res Sociol Health Care. 1998;15:147-171.14. Aiken LH, Clarke SP, Sloane DM. Hospital staffing,organizational support,andqualityofcare:cross-nationalfindings. Int J Qual Health Care. 2002;14:5-13.15. AHA Annual Survey Database. 1999 ed. Chi-cago, Ill: American Hospital Association; 1999.16. Commonwealth of Pennsylvania. Hospital Ques-tionnaire: Reporting Period July 1, 1998-June 30,1999. Harrisburg, Pa: Department of Health, Divi-sion of Statistics.17. Needleman J, Buerhaus PI, Mattke S, Stewart M,Zelevinsky K. Nurse staffing and patient outcomes inhospitals. Available at: http://bhpr.hrsa.gov/nursing/staffstudy.htm. Accessed August 6, 2002.18. Ayanian JZ, Weissman JS, Chasan-Taber S, Ep-stein AM. Quality of care for two common illnessesin teaching and nonteaching hospitals. Health Aff(Millwood). 1998;17:194-205.19. Hartz AJ, Krakauer H, Kuhn EM, et al. Hospitalcharacteristics and mortality rates. N Engl J Med. 1989;321:1720-1725.

20. Asch DA, Jedrziewski MK, Christakis NA. Re-sponse rates to mail surveys published in medical jour-nals. J Clin Epidemiol. 1997;50:1129-1136.21. The Registered Nurse Population. Rockville, Md:US Dept of Health and Human Services; 1996.22. Maslach C, Jackson SE. Burnout in health profes-sions: a social psychological analysis. In: Sanders GS, SulsJ, eds. Social Psychology of Health and Illness. Hillsdale,NJ: Lawrence Erlbaum Associates; 1982:227-251.23. Maslach C, Jackson SE. Maslach Burnout Inven-tory Manual. 2nd ed. Palo Alto, Calif: Consulting Psy-chologists Press; 1986.24. Silber JH, Williams SV, Krakauer H, Schwartz JS.Hospital and patient characteristics associated withdeath after surgery: a study of adverse occurrence andfailure to rescue. Med Care. 1992;30:615-629.25. Silber JH, Rosenbaum PR, Ross RN. Comparingthe contributions of groups of predictors: which out-comes vary with hospital rather than patient charac-teristics? J Am Stat Assoc. 1995;90:7-18.26. Silber JH, Rosenbaum PR, Schwartz JS, Ross RN,Williams SV. Evaluation of the complication rate as ameasure of quality of care in coronary artery bypassgraft surgery. JAMA. 1995;274:317-323.27. Silber JH, Kennedy SK, Even-Shoshan O, et al. An-esthesiologist direction and patient outcomes. Anes-thesiology. 2000;93:152-163.28. Silber JH, Rosenbaum PR, Trudeau ME, et al. Mul-tivariate matching and bias reduction in the surgicaloutcomes study. Med Care. 2001;39:1048-1064.29. Silber JH, Kennedy SK, Even-Shoshan O, et al. An-esthesiologist board certification and patient out-comes. Anesthesiology. 2002;96:1044-1052.30. Hanley JA, McNeil BJ. The meaning and use ofthe area under a receiver operating characteristic (ROC)curve. Radiology. 1982;143:29-36.31. Huber PJ. The Behavior of Maximum Likeli-hood Estimates Under Non-standard Conditions: Pro-ceedings of the Fifth Berkeley Symposium on Math-ematical Statistics and Probability. Berkeley: Universityof California Press; 1967:221-233.32. White H. Maximum likelihood estimation of mis-specified models. Econometrica. 1982;50:1-25.33. Hosmer DW, Lemeshow S. Applied Logistic Re-gression. New York, NY: John Wiley & Sons Inc; 1989.34. Bishop YM, Fienberg SE, Holland PW. DiscreteMultivariate Analysis: Theory and Practice. Cam-bridge, Mass: MIT Press; 1975.35. Agresti A. Categorical Data Analysis. New York,NY: John Wiley & Sons Inc; 1990.36. Shortell SM, Hughes EFX. The effects of regula-tion, competition, and ownership on mortality ratesamong hospital inpatients. N Engl J Med. 1988;318:1100-1107.37. Institute of Medicine. Nursing Staff in Hospitalsand Nursing Homes: Is It Adequate? Washington, DC:National Academy Press; 1996.38. Aiken LH, Sloane DM, Lake ET, Sochalski J, We-ber AL. Organization and outcomes of inpatient AIDScare. Med Care. 1999;37:760-772.39. Mitchell PH, Shortell SM. Adverse outcomes andvariations in organization of care delivery. Med Care.1997;35(suppl 11):NS19-NS32.40. Moses LE, Mosteller F. Institutional differences inpostoperative death rates. JAMA. 1968;203:492-494.41. Pronovost PJ, Jenckes MW, Dorman T, et al. Or-ganizational characteristics of intensive care units re-lated to outcomes of abdominal aortic surgery. JAMA.1999;281:1310-1317.42. Needleman J, Buerhaus P, Mattke S, Stewart M,Zelevinsky K. Nurse-staffing levels and the quality ofcare in hospitals. N Engl J Med. 2002;346:1715-1722.43. Audit Commission. Acute Hospital Portfolio: Re-view of National Findings: Ward Staffing. London, En-gland: The Audit Commission; 2001:3.44. Nursing Executive Committee. Reversing the Flightof Talent: Nursing Retention in an Era of GatheringShortage. Washington, DC: Advisory Board Co; 2000.



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