Nurse absenteeism and workload: negative ... - Wiley Online Library

JAN

ORIGINAL RESEARCH

Nurse absenteeism and workload: negative effect on restraint use, incident reports and mortality Lynn Unruh, Lindell Joseph & Margaret Strickland Accepted for publication 1 August 2007

Correspondence to L. Unruh: e-mail: lunruh@mail.ucf.edu Lynn Unruh PhD RN LHRM Fellow in Nursing Policy and Philanthropy Robert Wood Johnson Foundation, Princeton, New Jersey, USA, and Associate Professor Health Services Administration, Department of Health Professions, College of Health and Public Affairs, University of Central Florida, Orlando, Florida, USA Lindell Joseph PhD RN Candidate and Nurse Researcher Center for Nursing Research and Innovation, Florida Hospital Medical Center, Orlando, Florida, USA Margaret Strickland BSN RN Chief Nursing Officer Florida Hospital Altamonte, Administrative Offices, Altamonte Springs, Florida, USA

U N R U H L ., J O S E P H L . & S T R I C K L A N D M . ( 2 0 0 7 ) Nurse absenteeism and workload: negative effect on restraint use, incident reports and mortality. Journal of Advanced Nursing 60(6), 673–681 doi: 10.1111/j.1365-2648.2007.04459.x

Abstract Title. Nurse absenteeism and workload: negative effect on restraint use, incident reports and mortality Aim. This paper is a report of a study to assess the impact of nurse absenteeism on the quality of patient care. Background. Nurse absenteeism is a growing management concern. It can contribute to understaffed units, staffing instability, and other factors that could have a negative impact on patient care. The impacts of absenteeism on the quality of nursing care have rarely been studied. Method. Retrospective monthly data from incident reports and staffing records in six inpatient units for 2004 were analysed. Dependent variables were the numbers of restraints, alternatives to restraints, incident reports, deaths, and length of stay. Explanatory variables were nurse absenteeism hours, patient days per nursing staff, and interaction between these variables. Controls were patient acuity and unit characteristics. Fixed effects regressions were analysed as regular or negative binomial models. Findings. Neither high Registered Nurse absenteeism nor high patient load was related to restraint use when taken separately. However, high Registered Nurse absenteeism was related to restraint use when patient load was high. Registered Nurse absenteeism was related to a lower use of alternatives to restraints. Incident reports were increased by high patient load, but not absenteeism, or absenteeism given patient load. When both patient load and absenteeism were high, deaths were higher also. Conclusion. Absenteeism alone may not be a strong factor in lowering quality, but the combination of high Registered Nurse absenteeism and high patient load could be a factor. Staffing and absenteeism may be part of a vicious cycle in which low staffing contributes to unit absenteeism, which contributes to low staffing, and so on. Keywords: incident forms, nurse absenteeism, nurse–patient relationships, quality of care, retrospective analysis, staffing records, work organization

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Introduction Nurse absenteeism is a fact of life of inpatient units but is problematic because it can contribute to understaffed units, staffing instability, chaotic working environments, staff demoralization, and other factors that could negatively impact on patient care (Adams & Bond 2003, Thomson 2005). In an effort to reduce contributors to the problem, some of the probable causes of absenteeism, such as personal and family illnesses, sickness absence policies and job dissatisfaction, have been identified. However, the impact of absenteeism on the quality of nursing care and patient outcomes have rarely been studied, and such information is important when advocating for changes in employment that could reduce absenteeism. Concerns over nurse absenteeism in a 258-bed suburban hospital in the United States of America (USA) led us to study of its effects on patient care and patient outcomes using inpatient data for the year 2004.

Background Absenteeism, defined simply as unscheduled absences, has been described as ‘nursing service’s albatross’ (Miller & Norton 1986, p. 38). The ‘albatross’ of absenteeism has been hard to overcome, and recently has been on the rise in US hospitals (Harter 2001). Although present among all levels of nurses, the extent to which it occurs may differ among types of nursing staff. In one study, it was found to be highest among nursing assistants and lowest among Registered Nurses (RN) (Burton 1992), although in other studies this differentiation has not been strongly noted (Adams & Bond 2003). Absenteeism is thought to disrupt the working environment, producing staffing instability and affecting employee morale (Taunton et al. 1989, Bauman et al. 2001, Thomson 2005). Nurse staffing instability, in turn, contributes to lower cohesion among nurses, less collaboration with physicians, and greater difficulties in handling workload (Adams & Bond 2003). Nursing shortages are accentuated by absenteeism (Rogers et al. 1990). Negative work environments in general are related to job dissatisfaction among nurses (Tumulty et al. 1994). In particular, inadequate staffing and heavy workload – which absenteeism may especially affect – have been found to be the major sources of job dissatisfaction (Aiken et al. 2002, Dunn et al. 2005, Khowaja et al. 2005, Sheward et al. 2005). Job dissatisfaction, in turn, can lead to absenteeism, turnover or professional exit (Parker & Kulik 1995, Shields & Ward 2001, Tzeng 2002, Gardulf et al. 2005). Absenteeism appears 674

to be part of a vicious cycle in which it contributes to a negative work environment, which then leads to more absenteeism and to other types of staffing instability, such as increased turnover. Furthermore, these hypothesized effects of absenteeism may affect patient care, and therefore patient outcomes (Taunton et al. 1989, Rogers et al. 1990, Adams & Bond 2003). In a 1995 interview study with nurses, Adams and Bond (1995) found that nurses put great importance on staff stability in generating effective working relations and quality patient care. In a second study, a strong connection between staff stability and professional nursing practice was found (Adams & Bond 2003). Inadequate staffing has been found to lead to poor performance, including increased errors (Arnetz 1999, Benner et al. 2002, Bridger 1997, Kalisch, 2006). In contrast, adequate staffing is associated with the provision of recommended medical care for patients with selected medical diagnoses (Landon et al. 2006). The negative patient outcomes of inadequate staffing include: adverse events, such as falls; bloodstream infections; pneumonia; urinary tract infections; pressure ulcers; failure to rescue (defined as the death of a patient having experienced a life-threatening complication); and patient death (ANA 2000, Aiken et al. 2002, Cho et al. 2003, Dunton et al. 2004, Elting et al. 2005, Krauss et al. 2005, Mark et al. 2002, Needleman et al. 2002, Person et al. 2004, Sales et al. 2005, Seago et al. 2006, Unruh 2003, Whitman et al. 2002). Nurse dissatisfaction, burnout, exhaustion and stress have also been associated with lower quality of care and negative patient outcomes (Dugan et al. 1996, Koivula et al. 1998, Laschinger & Leiter 2006). Dugan et al. (1996) found a relationship between nurse stress and patient falls and medication errors. Koivula et al. (1998) reported that nurse exhaustion prevented nurses from having necessary prerequisites for quality improvement, while Laschinger and Leiter (2006) found that emotional exhaustion was related to greater patient adverse events. Although a negative relationship between nurse absenteeism and the quality of patient care can be hypothesized through theoretical linkages and studies above, very few studies have examined this relationship directly. Roszkowski et al. (2005) noted that absenteeism was related to nurses’ perceptions of poorer performance (both self-rated and manager-rated). In Taunton et al. (1994) it was found that absenteeism was related to higher rates of nosocomial urinary tract and bloodstream infections in hospitalized patients. We are not aware of other studies of the impact of absenteeism on patient outcomes or the quality of care. A review of the literature by Thomson (2005) corroborates


JAN: ORIGINAL RESEARCH

Nurse absenteeism a negative effect

our lack of findings. The lack of information on the effect of absenteeism on the quality of patient care and patient outcomes points to the need for more research in this area.

The study Aim The aim of the study was to assess the impact of nurse absenteeism on the quality of patient care. The study hypotheses were: • Higher levels of absenteeism have a negative impact on the quality of patient care. • Absenteeism has a more negative effect on quality in units that also have lower staffing levels. • Lower staffing levels have a negative impact on the quality of care.

Design A retrospective study was carried out using unit-level data from incident reports and staffing records for 2004.

Data sources This study was conducted at a 258-bed hospital which is part of a large hospital system in the southeast USA. Data from the following six inpatient units were used: 17-bed intensive care, 24-bed oncology, 39-bed medical–surgical, 39-bed neuro-medical progressive care, 46-bed pulmonary progressive care, and 46-bed cardiac progressive care. Available data were recorded as rates or numbers per unit in monthly intervals. There was a maximum of 12 monthly observations for the six units, which equalled 72 observations when all data were present.

Measures Table 1 presents the measures used in the study, their definitions, and the completeness of the data. The dependent variables were nursing process and patient outcomes indicators collected in hospital clinical performance improvement reports, incident reports or risk management databases for which data were complete or nearly complete and for which sufficient variability existed. Despite a large number of indicators, most data were too incomplete to use or lacked

Table 1 Study measures Variable

Definition

Source

Consistent data available

Dependent variables Restraints

No. episodes of restraint use

Incident reports

Alternatives to restraints

No. alternatives to restraints used

Incident reports

Incident reports

No. incident reports per unit per no. patient days of care per unit No. deaths Length of stay

Risk management reports Incident reports Risk management reports

Missing some months in some units, n = 67 Missing some months in some units, n = 67 Yes, n = 72

No. hours of unplanned absences per unit per month for RNs, LPNs, NAs, Total nursing staff No. hours worked by each type of nursing staff in the month per the number of patient days for that unit Average age of RN per unit

Staffing records

Yes, n = 72

Staffing records

Yes, n = 72

Staffing records

Yes, but no variation in average age by unit (most around 43) Yes, but little variation by unit (most around 2) Yes, n = 72

Deaths Length of stay Explanatory variables and controls RN, LPN, NA absenteeism hours

RN, LPN, NA worked hours per patient days Age of RNs on unit RN clinical level Average daily census Case mix Unit ID

Average unit RN clinical care level (I–IV) Average no. patients per day Average unit case mix for the time period ID number for each FHA unit

Staffing records Risk management reports Staffing records Staffing records

Yes, n = 72 Yes, n = 72

Yes, n = 72 Yes, n = 72

RN, Registered Nurses; LPN, Licensed Practical Nurses; NA, nursing assistants. 2007 The Authors. Journal compilation 2007 Blackwell Publishing Ltd

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variability across hospital units. The acceptable data on the nursing process were the number of restraints and alternatives to restraints (67 observations each), while those for patients outcomes were the number of incident reports, deaths and length of stay (a full set of 72 observations each). The first three of these five measures are direct results of nursing care and therefore have face and content validity as indicators of the quality of nursing care. Mortality has been demonstrated in prior studies as a nursing-sensitive quality measure (Aiken et al. 2002). Length of stay has likewise been linked to nursing care (Shamian et al. 1994, Czaplinski & Diers 1998). As a result, the study was conducted using these five dependent variables. The explanatory variables of interest were the number of absenteeism hours among each type of nursing staff [RN, Licensed Practical Nurses (LPN), and nursing assistants (NA)], as listed in Table 1. Another set of explanatory variables was ratios of the number of worked hours for each type of nursing staff divided by the number of patient days. As the study involved six inpatient units, unit-level controls were needed. The important ones were staff demographics, such as age, gender, race and ethnicity, levels of education, and years of experience. However, the only unit characteristics provided by the hospital records were RN age and clinical level. Patient characteristics which could contribute to patient outcomes and which therefore needed to be controlled were captured in a unitlevel case-mix variable.

Ethical considerations As this was a retrospective study involving a convenience sample of archival data, no participant recruitment was required, and case selection was dependent on the availability and completeness of the data in the records. There were no clinical risks to the patients. Confidentiality risks, because of researcher access to patient-level data, were dealt with by stripping the data of all patient identifiers prior to researcher involvement. The study was judged to be exempt from full Institutional Review Board review by the Boards at both the University of Central Florida and the participating hospital.

Statistical analysis All the data were collected by the hospital’s reporting systems and were in electronic form. The data existed in separate data sets. Data in each file were cleaned and prepared for merging with the other data. Specific measures of interest were created from the existing raw data through 676

manipulations in Excel or recoding in Statistical Analysis System (SAS) 9.1 (SAS n.d.). For example, nursing staff absenteeism hours, which were defined as the number of hours of unplanned absences, were a summation of several categories of unplanned absences (sick time, no shows, etc.). Once the data were ready for analysis, SAS was used to analyse the functional models. Descriptive statistics were calculated for the absenteeism and staffing variables, controls and dependent variables (see Table 2). For the associative analysis, we ran regressions for all types of staff. We were interested in the impact of each type of nursing staff absenteeism on each of the dependent variables listed in Table 1. We were also interested in how absenteeism affected the dependent variables, given unit staffing. This required the use of an interaction term between absenteeism hours and staffing levels, as is indicated in the statistical model below. Finally, we also examined how staffing impacts the nursing process and patient outcomes. The use of an interaction term required that the two variables in the term had values that went in the same direction. As increases in absenteeism indicate a negative change while increases in nurse staffing indicate a positive change, to be able to interpret the regressions with interaction between these variables, we used the inverse of the nurse staffing variables in the regressions. This transformed the original ‘nursing hours per patient days’ into ‘patient days per nursing hours,’ analogous to patient load or patients per nurse. Thus, for the purposes of the regressions, increases in absenteeism and in patient load ran in the same direction. Except for length of stay, the dependent variables were incidences of events (‘count data’) that tend to occur infrequently. This means that the values were skewed towards 0 and were not normally distributed. Turning this count data into rates by weighting them by patient days of care would result in the same non-normal, skewed distribution. Our descriptive analysis of the dependent variables corroborated this fact: the incidences and rates of restraints, alternatives to restraints, incident reports and deaths were clustered around zero, skewed and highly dispersed (variance greater than the mean), whereas the length of stay was fairly normally distributed (see Table 2). Given these characteristics of the dependent variables, except for length of stay, ordinary least squares regressions could not be used. Instead, we used exponentially based regressions. For the numbers of restraints, alternatives to restraints and incident reports, a negative binomial regression, which takes into account highly dispersed distributions, was used. For the number of deaths, we found that a Poisson regression had better fit. Both the negative binomial and Poisson regression require that the dependent variables are in ‘count’ form rather than being a




Table 2 Descriptive statistics: average monthly unit values* Variable name

Mean

RN absenteeism hours 215Æ36 RN absenteeism hours 0Æ04 per total worked hours RN absenteeism hours 0Æ31 per patient day RN worked hours 7Æ51 per patient day LPN absenteeism hours 41Æ60 LPN absenteeism hours 0Æ059 per total worked hours LPN absenteeism hours 0Æ038 per patient day LPN worked hours 0Æ53 per patient day NA absenteeism hours 136Æ80 NA absenteeism hours 0Æ047 per total worked hours NA absenteeism hours 0Æ16 per patient day 2Æ97 NA worked hours per patient day RN age 43Æ16 RN clinical level 2Æ04 (average of 1–4) Patient days, all units 890Æ92 Patient days, unit 1 571Æ17 Patient days, unit 2 1,164Æ25 Patient days, unit 3 980Æ91 Patient days, unit 4 1,152Æ17 Patient days, unit 5 1,052Æ58 Patient days, unit 6 424Æ42 CMI, all units 1Æ97 CMI, unit 1 1Æ76 CMI, unit 2 1Æ29 CMI, unit 3 1Æ54 CMI, unit 4 1Æ27 CMI, unit 5 1Æ49 CMI, unit 6 4Æ46 No. of restraints 11Æ87 No. of alternatives 6Æ75 to restraints No. of incident reports 12Æ43 No. of deaths 4Æ02 Length of stay 3Æ61

SD 110Æ93 0Æ02

Minimum Maximum 8 0Æ001

520 0Æ08

0Æ29

0Æ01

1Æ30

4Æ03

3Æ89

19Æ56

48Æ89 0Æ062

0 0

172Æ00 0Æ30

0Æ04

0

0Æ14

0Æ48

0

2Æ01

96Æ85 0Æ039

0 0

392Æ00 0Æ16

0Æ16

0

0Æ85

1Æ51

0

5Æ36

1Æ95 0Æ10

38 1Æ85

46 2Æ2

299Æ04 330Æ00 1,329Æ00 122Æ21 463Æ00 933Æ00 95Æ15 1,022Æ00 1,329Æ00 45Æ97 912 1,057Æ00 59Æ62 1,079Æ00 1,239Æ00 51Æ74 964Æ00 1,148Æ00 51Æ02 330Æ00 520Æ00 1Æ36 1Æ16 7Æ27 0Æ38 1Æ35 2Æ48 0Æ09 1Æ16 1Æ41 0Æ11 1Æ39 1Æ73 0Æ06 1Æ18 1Æ39 0Æ13 1Æ29 1Æ76 1Æ83 2Æ15 7Æ27 9Æ14 1 40 5Æ35 0 23 6Æ17 3Æ51 0Æ65

2 0 2Æ60

31 17 5Æ40

*Average of all units’ monthly values unless otherwise indicated. CMI, Case-Mix Index; RN, Registered Nurses; LPN, Licensed Practical Nurses; NA, nursing assistants; SD, standard deviation.

rate. A weighting factor in the regression accounted for the unit size, patient volume or staff volume. Descriptive analysis also revealed that there was very little variation in RN characteristics (age and clinical level). These variables were therefore not included in the models as they would not have added anything to the analysis.

Statistical models also took into account the fact that measures were taken on the same six units 12 times (monthly for the 12 months). This type of ‘repeated measures’ analysis, which causes a heteroskedasticity bias, can be corrected by adding a fixed effects variable. This adjustment also allowed us to control for all individual unit characteristics, although we did not know what the specific characteristics were. The basic statistical model was: Nursing care quality = b1 + b2*(RN, LPN and NA absenteeism) + b3*(RN, LPN and NA patient) + b4*(RN, LPN and NA absenteeism* RN, LPN and NA patient load) + b5*(CMI) + ui (Fixed Effect = unit) + e. Five separate regressions were analysed for each nursing staff type corresponding to the five dependent variables listed in Table 1. Each type of nursing staff was analysed in a separate set of regressions because they were highly intercorrelated in statistical analyses. Therefore, 15 total regressions were performed. For the normally distributed dependent variable (length of stay), the regressions were analysed using a regular linear-fixed effects analysis (using the ‘mixed’ procedure in SAS) following the basic statistical model above. For the rest of the dependent variables that were skewed and dispersed, the regressions were analysed as log-linear functions of a negative binomial-fixed effects model (using the Generalized Linear Model GENMOD procedure in SAS). In this log-linear model, the dependent variables are counts of the incidents, and the logs of these counts are regressed on the log of a weighting factor and the explanatory and control variables using a negative binomial distribution. For example, in the model below, ‘RNTWHR’ (total number of RN hours worked in the unit in the month) is the weighting factor, x’i are the explanatory, interaction and control variables (nurse absenteeism, nurse patient load, nurse absenteeism*nurse patient load and CMI), and ui are the fixed effect unit variables: Log{nursing care quality} = log{RNTWHR} + xi¢b + ui + e

Results Table 2 reports the results of the descriptive analysis. Average RN absenteeism hours per month were 215. This varied from only 8 hours in one unit to 520 in another, resulting in a standard deviation (SD) of 111. As these results were not weighted by the varied unit staffing levels or patient volume, we also calculated RN absenteeism hours per total scheduled hours and per patient day. These also showed large variance, as can be seen in Table 2. Mean RN worked hours per patient day (RN staffing) were 7Æ5, with a SD of 4. LPN and NA absenteeism hours were less than those of RN, but only


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because their worked hours were also less. When weighted for worked hours or patient days, both LPNs and NAs had higher rates of absenteeism than RN. RN age averaged 43 years, while clinical level was 2 (out of 1–4). As Table 2 indicates, these variables had very little variation (SD of 1Æ95 for age and 0Æ10 for clinical level), justifying their removal from the statistical model. Monthly patient days were 891 on average, but two units (1 and 6) had many fewer on the average (571 and 424 respectively). Average Case-Mix Index (CMI) was 1Æ97, with a large SD of 1Æ36. The large variation was because of a high average CMI of 4Æ46 for unit 6. The maximum for unit 6 was 7Æ27, indicating that during 1 month their CMI was extremely high. Regarding quality indicators, there was an average of 12 restraint use incidents per unit per month, seven incidents of the use of alternatives to restraints, 12Æ5 incident reports, and four deaths. Most indicators had SD nearly as large as the mean, indicating a large variance and dispersion of the values, most likely because of the fact that some units, such as the ICU/PCU had more acutely ill patients than others. Length of stay was 3Æ61 days on average. Table 3 reports the results of the regressions for RN staff. Although regressions were also run separately for LPNs and NAs, none showed statistically significant results and so these are not reported. Concerning RN staff, neither high absenteeism nor high patient load was related to the use of restraints when taken separately. However, high RN absenteeism in combination with a high patient load was associated with significantly higher use of restraints.

In contrast, high RN absenteeism was related to lower use of alternatives to restraints. In this context, alternatives to restraints are positive actions taken in place of the use of restraints, so the lower incidence of alternatives to restraints associated with absenteeism is a negative indicator of nursing care quality. This result was counterbalanced, however, by the interaction term, which counter-intuitively was positively related to the use of restraint alternatives. The results indicate that the numbers of incident reports was increased by high patient load, but not absenteeism or the interaction of absenteeism and patient load. The findings on number of deaths were partially counter-intuitive: the greater the RN absenteeism, the lower the number of deaths. RN patient load was not related to number of deaths. However, the statistically significantly positive interaction term indicates that if both patient load and absenteeism are high, the number of deaths will be high also. This result would counter-balance the counter-intuitive negative impact of absenteeism on deaths. Finally, no absenteeism or staffing variables were statistically significantly related to length of stay. CMI was statistically significantly related to all quality indicators except number of incident reports. High case mix was associated with high restraint use and high length of stay, but low use of alternatives to restraints and a low number of deaths. The fixed effects term (units 1–6) in each of these regressions yielded coefficients for each unit for each dependent variable. To maintain unit anonymity, in this report we resorted and relabelled the units 1 through 6 and used unit number 6 for our regression reference unit. Units 1 through 5

Table 3 Impact of absenteeism on patient outcomes

Explanatory variable: RN patient load RN absenteeism hours RN absenteeism hours given RN patient load CMI Unit 1 Unit 2 Unit 3 Unit 4 Unit 5 Unit 6

No. of restraints n = 67

No. of alternatives to restraints n = 67

No. of incident reports n = 72

No. of deaths n = 72

Length of stay n = 72

Est

Est

Est

Est

Est

NS NS 0Æ0103**

NS 0Æ0013* 0Æ0126**

5Æ4561* NS NS

NS 0Æ0050**** 0Æ0330***

NS NS NS

0Æ0305*** 1Æ7960**** 1Æ2323**** 1Æ4560**** 0Æ6170* 0Æ8024**** –

0Æ0967**** 1Æ8962**** 1Æ5294**** 1Æ7613**** 1Æ1435** 1Æ2694**** –

NS NS NS NS NS NS –

0Æ0302**** 1Æ4005**** 2Æ4830**** 3Æ4998**** 2Æ6870**** 2Æ7689**** –

0Æ09779* NS NS NS NS NS –

RN patient load = the inverse of RN hours per patient days = patient days per RN hours. Reference unit in relationship to all other units. NS, not statistically significant; CMI, Case-Mix Index; RN, Registered Nurses; LPN, Licensed Practical Nurses; NA, nursing assistants. *P < 0Æ05; **P < 0Æ01; ***P < 0Æ001; ****P < 0Æ0001.

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What is already known about this topic • Nurse absenteeism may disrupt the working environment, contributing to greater difficulties in handling workload and affecting employee morale. • Negative work environments, particularly insufficient staffing, are related to nurse dissatisfaction, absenteeism and turnover; poorer nurse performance; and worse patient outcomes. • Nurse absenteeism may be related to higher rates of nosocomial urinary tract and bloodstream infections in hospitalized patients.

What this paper adds • High Registered Nurse absenteeism was related to higher restraint use and more patient deaths when patient load is also high. • High Registered Nurse absenteeism was independently associated with fewer uses of alternatives to restraints. • High patient load was related to greater numbers of incident reports.

had lower restraint use, lower alternatives to restraint use, and fewer deaths than unit 6, the reference unit. No one unit stood out compared with another in terms of incident reports or length of stay.

Discussion Of the five quality indicators in this study, nurse absenteeism was related to only one: lower use of alternatives to restraints. However, the combination of high RN absenteeism and high patient load was related to greater use of restraints and a higher number of deaths. This could mean that it is not absenteeism alone that contributes to lower quality of nursing care, but absenteeism on units that have poor staffing. Further, as mentioned in the introduction, staffing and absenteeism may be part of a vicious cycle in which low staffing contributes to unit absenteeism, which then contributes to low staffing, and so forth. Therefore, despite the lack of robust findings, this study supports others that point to RN staffing as important in maintaining quality of care, and it gives tentative indications that absenteeism also plays a role. In addition, some results were counter-intuitive: a higher use of alternatives to restraints was found on units with both higher RN absenteeism and higher RN patient load, while RN absenteeism (unrelated to patient load) was associated


with fewer patient deaths. The lower mortality associated with higher absenteeism may be an indication of the lack of an independent association between absenteeism and mortality. Although it may be tempting to hypothesize that uncontrolled unit characteristics contributed to confounding results, the effects of unit characteristics should have been controlled through the CMI variable and the fixed effects unit variable. It is possible that the results for absenteeism were not more robust because we worked with monthly, instead of daily or shift, associations. This was necessary because our absenteeism and staffing measures were reported on a pay-period (every 2 weeks) basis, while the patient care and patient outcomes measures were reported on a monthly basis. This necessitated the use of monthly aggregations of absenteeism and staffing data to match the monthly quality data. Yet the effects of absenteeism and staffing are much more directly connected in reality. The higher use of restraints, the lower use of alternatives to restraints, and increases in incident reports occurred in the same shift as the absenteeism and/or staffing problem, while mortality and length of stay changes showed up within hours or days of the problems. Aggregating the explanatory and response variables to monthly values diluted this connection, probably weakening the association in the process. This data specification problem was a likely primary reason why we did not see more statistically significant results. What is really needed to test the relationship between absenteeism and patient outcomes is shift (or daily) absenteeism and staffing data linked to patient outcomes flagged by the time of day. For example, such a study would look at whether the higher number of absentee hours in a shift or a day on a unit was related to a certain type of patient outcome (code blue, medication error, fall, etc.) during that shift or day on that unit. Another probable reason why we did not have statistically significant results was that the monthly aggregation of data resulted in a smaller number of observations. As we could not study shift, daily or weekly measures, the monthly measures we used meant that the number of observations was reduced to 72 at the maximum (six units · 12 months). Then, for some patient outcomes, there were missing values that reduced the number of observations to 67. Out of concern for the low number of observations, we conducted a retrospective power analysis of the study. Software for conducting a power analysis of an exponential distribution analysed through a negative binomial regression does not exist. However, we used the GLMPower analysis in SAS, which is a power analysis of generalized linear models. The negative binomial regression is in the family of generalized linear models. The power analysis indicated that, with


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observations of 67 and 72 and with one categorical variable (fixed effects unit variable) and four covariates, the power was adequate for hypothesis-testing at the 5% level. A final limitation of our study was that restraint use and alternatives to restraints are quality indicators that may be more appropriate for some units than others. For example, one of the units in our study was a medical–surgical ICU, in which restraint use may be a more acceptable practice and alternatives to restraints less commonly practised. Therefore, in this unit these may not be good indicators of negative and positive quality respectively. Given our tentative results, and the limitations of the study which could have led to the weakness of those results, we believe that the issue of absenteeism and quality of care remains open. Additional research needs to be conducted using more types of nursing processes and patient outcomes indicators, with a larger number of observations, and more complete and more detailed data.

Conclusion As this was a case study of one hospital, and the results were not robust, they cannot be readily generalized and policy recommendations would be premature. However, it is important to understand the issue of absenteeism and its impact on patient outcomes, and this study has begun to investigate the issue. We see this study as an exploration that gives initial results and identifies data needs and requirements for future studies. Several recommendations for further research can be made. Research on the relationship between nurse absenteeism and the quality of care should be conducted with more detailed data on absenteeism, staffing and several different types of nursing-sensitive quality indicators. The indicators could be those used by the Joint Commission on the Accreditation of Healthcare Organizations (Joint Commission 2007), National Quality Forum (NQF 2003), or other quality improvement programmes in which the hospital may participate. Indicators should have face, content, or criterion validity as nursing-sensitive quality measures. The additional requirement for these studies, however, is that the unit, hour, and date of the incidents are flagged so that they can be linked with unit-level daily absenteeism and staffing data. In addition to a single hospital setting, it is important that these studies also be conducted in multiple hospital settings to be able to better generalize research results. The research should also be conducted in other institutional settings, such as nursing homes. In the introduction, we mentioned the lack of information on other possible absenteeism effects: on the work environ680

ment and nursing staff, and on hospital financial performance. These are other areas also in need of further research. The same methodologies would apply: detailed data, nursingsensitive quality indicators, and single, system or multihospital samples. There is a dearth of information on the effect of absenteeism on the workplace, the workforce, patient care, patient outcomes and financial performance. Future research on any one of these would contribute immensely to our understanding of the impacts of absenteeism.

Author contributions LU, LJ and MS were responsible for the study conception and design and the drafting of the manuscript. LJ performed the data collection and LU performed the data analysis. LJ and MS obtained funding and LJ provided administrative support. LU, LJ and MS made critical revisions to the paper. LU provided statistical expertise. LU supervised the study.

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