- Email: [email protected]
Isolation ward access block
Healthcare Infection 2009; 14: 47–50
Isolation ward access block Anthony Morton1,2,3 MS, MD Michael Whitby1 FRACP, FRCPA David Looke1 FRACP, FRCPA 1
Infection Management Services, The Princess Alexandra Hospital, Ipswich Road, Woolloongabba, Qld 4102, Australia. The School of Mathematical Sciences, The Queensland University of Technology, 2 George Street, Brisbane, Qld 4000, Australia. 3 Corresponding author. Email: [email protected] 2
Abstract There is overwhelming evidence that hospitals become unsafe with persistent high bed occupancy. Potentially preventable deaths occur and length of stay is prolonged. Infections and colonisations with multiple antibiotic-resistant organisms increase. It is probable that overall long-term efficiency is impaired. Isolation ward access block may be one of the mechanisms by which transmission of multiple antibioticresistant organisms is enhanced.
Introduction Hospital-acquired infections are a major threat to patient safety. In addition, they impair efficiency by increasing hospital length of stay and hospital costs. Multi-resistant methicillin-resistant Staphylococcus aureus (mMRSA) is the chief cause of these hospitalacquired infections. Hospital overcrowding is known to be associated with increases in hospital mortality1,2 and inpatient length of stay3 as it contributes to unsafe care of acutely ill patients requiring urgent admission, usually referred to as access block.4 In addition, it has been implicated in increased numbers of mMRSA infections.5,6 Recent reviews have emphasised the need for better systems for controlling mMRSA transmission, with a particular emphasis on hand hygiene,7 10 and there is evidence that hand hygiene may become less effective when there is overcrowding.11 Access block involving isolation wards appears to be one mechanism by which excessive bed occupancy can promote mMRSA colonisation.
Methods The Princess Alexandra Hospital, Brisbane, Australia, performs active patient screening (~1700 per month)12 and attempts to isolate all cases of mMRSA. Data are collected on all new mMRSA isolates and these are reviewed each month. We have examined these data between January 1999 and February 2008 using bar and run charts, generalised additive models (GAM) and, when there is autocorrelation, generalised additive mixed models (GAMM), and have related them to three periods when the isolation ward bed occupancy was a particular problem. In addition, we have examined monthly average mMRSA prevalence (mMRSA burden) between November 2003, when these data were first Ó Australian Infection Control Association 2009
collected, and February 2008. All analyses were performed using the R statistical software program.13 Smoothed predicted values for each time series were obtained using a Quasi-Poisson GAM or GAMM that included a cubic regression spline from the mgcv package in R.14 Because denominator data were available, the dependent variable for the new isolate data was the monthly rate and the denominator occupied bed days were entered into the model as weights. Denominator data were not employed with the monthly average prevalence data. The new isolate data also demonstrated autocorrelation so an AR1 autocorrelation term was included and the mixed GAMM model employed.14 The monthly average prevalence data did not display autocorrelation so a GAM model was used.
Results The bar and run chart (Figure 1) shows a decline in monthly new mMRSA isolates between 1999 and 2004. This was followed by a rise between 2004 and the first half of 2006, and a further decline after June 2006. The data displayed marked overdispersion (mean 15.4, variance weighted by the bed-days denominators 55.5). Figure 2 is a correlogram that suggests a first-order autoregressive (AR1) process. An AR1 autocorrelation term was found to be significant. The presence of trends, autocorrelation and overdispersion made interpretation of conventional control charts difficult. Figure 3 shows the smoothed predicted values for the new isolate data time series (centre line), with the outer lines representing the 95% precision for the predicted values. There is also an approximate control limit line at the 97.725th percentile of the Poisson distribution that is approximately equivalent to two standard deviations above the fitted line. This line DOI: 10.1071/HI09102
47
Healthcare Infection
A. Morton et al.
15000
Denominators
0
0
5000
10000
20 10
Counts
30
20000
40
25000
Figure 1. Monthly new multi-resistant methicillin-resistant Staphylococcus aureus isolates. Bar chart from January 1999 to February 2008. Hatched lines, data with smoothing; grey line, median; upper solid lines, denominators with smoothing.
Jan 99 Sep 99 Jun 00 Feb 01 Oct 01 Jun 02 Feb 03 Oct 03 Jun 04 Feb 05 Oct 05 Jun 06 Feb 07 Oct 07
Figure 2. (a) Autocorrelation function: monthly new methicillin-resistant Staphylococcus aureus isolates (significance indicated by solid line). (b) Partial autocorrelation function: monthly new methicillin-resistant Staphylococcus aureus isolates (significance indicated by solid line). (a) ACF
0.8 0.4 0.0 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Lag Partial ACF
(b) 0.8 0.4 0.0 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Lag
Figure 3. Monthly new multi-resistant methicillin-resistant Staphylococcus aureus isolates. Generalised additive mixed model ), data; ( ), fitted GAMM; ( ), 95% CI; ( ), control limit. (GAMM) chart from January 1999 to February 2008. (
Rate per 1000 occupied bed-days
A
B
Arrow shows when extra cohorting beds became available
1.5
C 1.0
0.5
Jan 99 Sep 99 May 00 Jan 01 Sep 01 May 02 Jan 03 Sep 03 May 04 Jan 05 Sep 05 May 06 Jan 07 Sep 07
48
Healthcare Infection
Isolation ward access block
identifies monthly values that may be atypical of the time series predicted values analogous to the control limits of a control chart.15 It shows clusters labelled A, B and C. Cluster A occurred in October and November 1999 when the isolation ward bed occupancy was dominated by a vancomycin-resistant Enterococcus outbreak and it was not possible to isolate all mMRSA carriers.16 Cluster B occurred between January and April 2001. There were 34 patients with multiple antibioticresistant Acinetobacter colonisation in January 2001 (eight new isolates) requiring isolation. An additional problem at this time was the move to a new hospital building with its associated disruption and reduced overall bed numbers. Cluster C occurred in the first half of 2006. Average bed occupancy of the 20-bed isolation ward between January 2006 and June 2006 was 107%. During this period there were, on average, 37 mMRSA carriers each day in the hospital, only 10 of whom could be accommodated in the 20-bed isolation ward, with the remainder nursed in their own wards, principally in side rooms. The other 10 isolation beds were, on average, occupied by carriers of other multiple antibiotic-resistant organisms, for example multiple antibiotic-resistant Acinetobacter. Many of these patients were long-stay patients. Figure 4 shows the average monthly prevalence data. The peak values occurred in the first half of 2006. Prevalence declined in the second half of 2006. The correlogram for these data (not shown) also suggested an AR1 autocorrelation process but an AR1 autocorrelation term was found to be not significant (presumably, this anomalous result was due to the presence of trends). The mean was found to be 28.9 and its variance 32.5. In June 2006 the hospital administration made available an additional half of a medical ward for cohorting colonised patients. Both new mMRSA isolates and mMRSA prevalence then declined. June 2006 is marked on Figures 3 and 4 with an arrow.
Discussion Although a determined effort is made to detect new mMRSA carriers, not all will be detected as it may not be cost-effective to employ universal screening in low-risk wards. However, the majority of new isolates are likely to have been detected, especially in the acute wards, and changes in the counts of new isolates should provide a good approximation to changes in the rates of colonisation. Transmission of hospital-acquired multiple antibiotic-resistant organisms is a complex multifactorial process involving the prevalence of the organism, the determination with which its presence is sought, bed occupancy and the ability of the hospital to isolate, discharge and decontaminate carriers, hygiene measures, especially hand hygiene,10,17 and antibiotic usage. These data therefore suggest, but do not prove, an association between isolation ward access block and increased transmission. However, isolation ward access block limits the ability of staff to isolate carriers. Figures 3 and 4 clearly show that, when isolation facilities were insufficient, mMRSA numbers increased. When additional beds were made available in a single ward for cohorting carriers, both prevalence of mMRSA within the hospital and the numbers of new mMRSA isolates promptly declined. Surprisingly, the earlier use of side rooms within their own wards to ‘isolate’ carriers appeared to have little or no impact on rising numbers. Cunningham and colleagues5,6 argue that hospital overcrowding results in insufficient time for hygiene measures such as disinfection of bed areas to occur. They also point out that the UK House of Commons Committee for Public Accounts has indicated that high levels of bed occupancy are not consistent with infection control and patient safety. They note that a high proportion of NHS hospitals in the UK operated at levels much
Figure 4. Monthly average multi-resistant methicillin-resistant Staphylococcus aureus prevalence. Generalised additive model chart ), data; ( ), fitted GAMM; ( ), 95% CI; ( ), control limit. from November 2003 to February 2008. ( 50
Arrow shows when extra cohorting beds became available
45
Counts
40 35 30 25 20 15 Nov 03
Mar 04
Jul 04
Nov 04
Mar 05
Jul 05
Nov 05
Mar 06
Jul 06
Nov 06
Mar 07
Jul 07
Nov 07
49
Healthcare Infection
higher than the Department of Health recommended mean level of 82%. The problem of excessive bed occupancy is therefore endemic in public hospital systems. Using a mathematical model, Beggs et al.11 predicted that, because nurse cohorting is disrupted by overcrowding, hand hygiene becomes less effective at limiting transmission. Hand hygiene is correctly a cornerstone of programs to limit mMRSA. If their model is correct, it is unwise to pursue bed usage practices that diminish the effectiveness of hand hygiene. Bagust et al.18 also used a mathematical model that predicted that patient safety is compromised by high levels of bed occupancy. It seems probable that the failure of the use of side rooms in general wards to control transmission may have been related to diminished effectiveness of hand hygiene in this situation. Recent studies by Sprivulis et al.1 and Richardson2 showed increased inpatient mortality when there is excessive bed occupancy but they do not explore the causes of this increased mortality. In addition, the study by Liew et al.3 does not examine the causes of increased length of stay. Further work is needed to provide this information. However, it seems probable that increased hospital-acquired infections may be one of the major causes.5,6 Furthermore, access block in isolation wards may be one of the consequences of excessive bed occupancy and, by enhancing transmission, also one of its causes. It seems probable that the short-term efficiency gained by running hospitals at near 100% capacity, as is currently common in Australia, may in fact increase costs overall. Since there is considerable ‘re-work’ because of increased numbers of adverse events that result in longer lengths of stay, the increased efficiency of continued high bed occupancy may well be an illusion. Further development of the simple analyses described here may enable administrators to estimate the minimum number of isolation beds that a hospital requires. In addition, they may provide a mechanism for determining the number of cohorting beds that may be required at any time, for example when multiple antibiotic-resistant organism numbers increase. The use of an approximate upper control limit based on the 97.725th percentile (equivalent to two standard deviations) of the Poisson distribution is somewhat empirical and requires further study. However, with the new isolate data, a limit based on the standard deviation of the 110 residuals (the differences between the values predicted by the model and the observed values) with three outliers excluded, leaving 107 normally distributed residuals also identified the three outlier groups labelled A, B and C in Figure 3. Outlier group A has been reported elsewhere.16 Outlier group B is not prominent in Figure 1 but the corresponding occupied bed days were reduced due to the hospital relocating.
50
A. Morton et al.
Outlier group C was the culmination of a significant upward trend that is clearly shown in Figure 1.
References 1.
Sprivulis P, Da Silva A, Jacobs I, Frazer A, Jelinek G. The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Med J Aust 2006; 184: 208–12.
2.
Richardson D. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust 2006; 184: 213–16.
3.
Liew D, Liew D, Kennedy M. Emergency department length of stay independently predicts excess inpatient length of stay. Med J Aust 2003; 179: 524–6.
4.
Cameron P. Hospital overcrowding: a threat to patient safety? Med J Aust 2006; 184: 203–4.
5.
Cunningham J, Kernohan W, Sowney R. Bed occupancy and turnover interval as determinant factors in MRSA infections in acute settings in Northern Ireland. J Hosp Infect 2005; 61: 189–93. doi:10.1016/ j.jhin.2005.04.014
6.
Cunningham J, Kernohan W, Rush T. Bed occupancy, turnover intervals and MRSA rates in English hospitals. Br J Nurs 2006; 15: 656–60.
7.
McCannon M, Hackbarth A, Griffin F. Miles to go: an introduction to the 5 million lives campaign. Jt Comm J Qual Patient Saf 2007; 33: 477–84.
8.
Ferguson J. A call to arms. Aust Infect Control 2007; 12: 39–40.
9.
Duckworth G. Controlling methicillin resistant Staphylococcus aureus. BMJ 2003; 327: 1177–8. doi:10.1136/bmj.327.7425.1177
10. Collignon P, Grayson L, Johnson P. Methicillin-resistant Staphylococcus aureus in hospitals: time for a culture change. Med J Aust 2007; 187: 4–5. 11. Beggs C, Noakes C, Shepherd S, Kerr K, Sleigh A, Banfield K. The influence of nurse cohorting on hand hygiene effectiveness. Am J Infect Control 2006; 34: 621–6. doi:10.1016/j.ajic.2006.06.011 12. Ferguson J. Healthcare-associated methicillin-resistant Staphylococcus aureus (MRSA) control in Australia and New Zealand. Aust Infect Control 2007; 12: 60–6. 13. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2008. Available online at: http://www.R-project.org [accessed February 2009]. 14. Wood S. Generalized additive models. Chapman & Hall/CRC: Boca Raton; 2006. 15. Morton A, Gatton M, Tong E, Clements A. New control chart methods for monitoring MROs in hospitals. Aust Infect Control 2007; 12: 14–18. 16. Bartley P, Schooneveldt J, Looke D, Morton A, Johnson D, Nimmo G. The relationship of a clonal outbreak of Enterococcus faecium vanA to methicillin-resistant Staphylococcus aureus incidence in an Australian hospital. J Hosp Infect 2001; 48: 43–54. doi:10.1053/jhin.2000.0915 17. Centers for Disease Control and Prevention. Guideline for Hand Hygiene in Healthcare Settings – 2002. Atlanta, GA: Centers for Disease Control and Prevention; 2002. Available online at: http:// www.cdc.gov/handhygiene/ [verified April 2009]. 18. Bagust A, Place M, Posnett J. Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. BMJ 1999; 319: 155–8.
Isolation ward access block Anthony Morton1,2,3 MS, MD Michael Whitby1 FRACP, FRCPA David Looke1 FRACP, FRCPA 1
Infection Management Services, The Princess Alexandra Hospital, Ipswich Road, Woolloongabba, Qld 4102, Australia. The School of Mathematical Sciences, The Queensland University of Technology, 2 George Street, Brisbane, Qld 4000, Australia. 3 Corresponding author. Email: [email protected] 2
Abstract There is overwhelming evidence that hospitals become unsafe with persistent high bed occupancy. Potentially preventable deaths occur and length of stay is prolonged. Infections and colonisations with multiple antibiotic-resistant organisms increase. It is probable that overall long-term efficiency is impaired. Isolation ward access block may be one of the mechanisms by which transmission of multiple antibioticresistant organisms is enhanced.
Introduction Hospital-acquired infections are a major threat to patient safety. In addition, they impair efficiency by increasing hospital length of stay and hospital costs. Multi-resistant methicillin-resistant Staphylococcus aureus (mMRSA) is the chief cause of these hospitalacquired infections. Hospital overcrowding is known to be associated with increases in hospital mortality1,2 and inpatient length of stay3 as it contributes to unsafe care of acutely ill patients requiring urgent admission, usually referred to as access block.4 In addition, it has been implicated in increased numbers of mMRSA infections.5,6 Recent reviews have emphasised the need for better systems for controlling mMRSA transmission, with a particular emphasis on hand hygiene,7 10 and there is evidence that hand hygiene may become less effective when there is overcrowding.11 Access block involving isolation wards appears to be one mechanism by which excessive bed occupancy can promote mMRSA colonisation.
Methods The Princess Alexandra Hospital, Brisbane, Australia, performs active patient screening (~1700 per month)12 and attempts to isolate all cases of mMRSA. Data are collected on all new mMRSA isolates and these are reviewed each month. We have examined these data between January 1999 and February 2008 using bar and run charts, generalised additive models (GAM) and, when there is autocorrelation, generalised additive mixed models (GAMM), and have related them to three periods when the isolation ward bed occupancy was a particular problem. In addition, we have examined monthly average mMRSA prevalence (mMRSA burden) between November 2003, when these data were first Ó Australian Infection Control Association 2009
collected, and February 2008. All analyses were performed using the R statistical software program.13 Smoothed predicted values for each time series were obtained using a Quasi-Poisson GAM or GAMM that included a cubic regression spline from the mgcv package in R.14 Because denominator data were available, the dependent variable for the new isolate data was the monthly rate and the denominator occupied bed days were entered into the model as weights. Denominator data were not employed with the monthly average prevalence data. The new isolate data also demonstrated autocorrelation so an AR1 autocorrelation term was included and the mixed GAMM model employed.14 The monthly average prevalence data did not display autocorrelation so a GAM model was used.
Results The bar and run chart (Figure 1) shows a decline in monthly new mMRSA isolates between 1999 and 2004. This was followed by a rise between 2004 and the first half of 2006, and a further decline after June 2006. The data displayed marked overdispersion (mean 15.4, variance weighted by the bed-days denominators 55.5). Figure 2 is a correlogram that suggests a first-order autoregressive (AR1) process. An AR1 autocorrelation term was found to be significant. The presence of trends, autocorrelation and overdispersion made interpretation of conventional control charts difficult. Figure 3 shows the smoothed predicted values for the new isolate data time series (centre line), with the outer lines representing the 95% precision for the predicted values. There is also an approximate control limit line at the 97.725th percentile of the Poisson distribution that is approximately equivalent to two standard deviations above the fitted line. This line DOI: 10.1071/HI09102
47
Healthcare Infection
A. Morton et al.
15000
Denominators
0
0
5000
10000
20 10
Counts
30
20000
40
25000
Figure 1. Monthly new multi-resistant methicillin-resistant Staphylococcus aureus isolates. Bar chart from January 1999 to February 2008. Hatched lines, data with smoothing; grey line, median; upper solid lines, denominators with smoothing.
Jan 99 Sep 99 Jun 00 Feb 01 Oct 01 Jun 02 Feb 03 Oct 03 Jun 04 Feb 05 Oct 05 Jun 06 Feb 07 Oct 07
Figure 2. (a) Autocorrelation function: monthly new methicillin-resistant Staphylococcus aureus isolates (significance indicated by solid line). (b) Partial autocorrelation function: monthly new methicillin-resistant Staphylococcus aureus isolates (significance indicated by solid line). (a) ACF
0.8 0.4 0.0 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Lag Partial ACF
(b) 0.8 0.4 0.0 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Lag
Figure 3. Monthly new multi-resistant methicillin-resistant Staphylococcus aureus isolates. Generalised additive mixed model ), data; ( ), fitted GAMM; ( ), 95% CI; ( ), control limit. (GAMM) chart from January 1999 to February 2008. (
Rate per 1000 occupied bed-days
A
B
Arrow shows when extra cohorting beds became available
1.5
C 1.0
0.5
Jan 99 Sep 99 May 00 Jan 01 Sep 01 May 02 Jan 03 Sep 03 May 04 Jan 05 Sep 05 May 06 Jan 07 Sep 07
48
Healthcare Infection
Isolation ward access block
identifies monthly values that may be atypical of the time series predicted values analogous to the control limits of a control chart.15 It shows clusters labelled A, B and C. Cluster A occurred in October and November 1999 when the isolation ward bed occupancy was dominated by a vancomycin-resistant Enterococcus outbreak and it was not possible to isolate all mMRSA carriers.16 Cluster B occurred between January and April 2001. There were 34 patients with multiple antibioticresistant Acinetobacter colonisation in January 2001 (eight new isolates) requiring isolation. An additional problem at this time was the move to a new hospital building with its associated disruption and reduced overall bed numbers. Cluster C occurred in the first half of 2006. Average bed occupancy of the 20-bed isolation ward between January 2006 and June 2006 was 107%. During this period there were, on average, 37 mMRSA carriers each day in the hospital, only 10 of whom could be accommodated in the 20-bed isolation ward, with the remainder nursed in their own wards, principally in side rooms. The other 10 isolation beds were, on average, occupied by carriers of other multiple antibiotic-resistant organisms, for example multiple antibiotic-resistant Acinetobacter. Many of these patients were long-stay patients. Figure 4 shows the average monthly prevalence data. The peak values occurred in the first half of 2006. Prevalence declined in the second half of 2006. The correlogram for these data (not shown) also suggested an AR1 autocorrelation process but an AR1 autocorrelation term was found to be not significant (presumably, this anomalous result was due to the presence of trends). The mean was found to be 28.9 and its variance 32.5. In June 2006 the hospital administration made available an additional half of a medical ward for cohorting colonised patients. Both new mMRSA isolates and mMRSA prevalence then declined. June 2006 is marked on Figures 3 and 4 with an arrow.
Discussion Although a determined effort is made to detect new mMRSA carriers, not all will be detected as it may not be cost-effective to employ universal screening in low-risk wards. However, the majority of new isolates are likely to have been detected, especially in the acute wards, and changes in the counts of new isolates should provide a good approximation to changes in the rates of colonisation. Transmission of hospital-acquired multiple antibiotic-resistant organisms is a complex multifactorial process involving the prevalence of the organism, the determination with which its presence is sought, bed occupancy and the ability of the hospital to isolate, discharge and decontaminate carriers, hygiene measures, especially hand hygiene,10,17 and antibiotic usage. These data therefore suggest, but do not prove, an association between isolation ward access block and increased transmission. However, isolation ward access block limits the ability of staff to isolate carriers. Figures 3 and 4 clearly show that, when isolation facilities were insufficient, mMRSA numbers increased. When additional beds were made available in a single ward for cohorting carriers, both prevalence of mMRSA within the hospital and the numbers of new mMRSA isolates promptly declined. Surprisingly, the earlier use of side rooms within their own wards to ‘isolate’ carriers appeared to have little or no impact on rising numbers. Cunningham and colleagues5,6 argue that hospital overcrowding results in insufficient time for hygiene measures such as disinfection of bed areas to occur. They also point out that the UK House of Commons Committee for Public Accounts has indicated that high levels of bed occupancy are not consistent with infection control and patient safety. They note that a high proportion of NHS hospitals in the UK operated at levels much
Figure 4. Monthly average multi-resistant methicillin-resistant Staphylococcus aureus prevalence. Generalised additive model chart ), data; ( ), fitted GAMM; ( ), 95% CI; ( ), control limit. from November 2003 to February 2008. ( 50
Arrow shows when extra cohorting beds became available
45
Counts
40 35 30 25 20 15 Nov 03
Mar 04
Jul 04
Nov 04
Mar 05
Jul 05
Nov 05
Mar 06
Jul 06
Nov 06
Mar 07
Jul 07
Nov 07
49
Healthcare Infection
higher than the Department of Health recommended mean level of 82%. The problem of excessive bed occupancy is therefore endemic in public hospital systems. Using a mathematical model, Beggs et al.11 predicted that, because nurse cohorting is disrupted by overcrowding, hand hygiene becomes less effective at limiting transmission. Hand hygiene is correctly a cornerstone of programs to limit mMRSA. If their model is correct, it is unwise to pursue bed usage practices that diminish the effectiveness of hand hygiene. Bagust et al.18 also used a mathematical model that predicted that patient safety is compromised by high levels of bed occupancy. It seems probable that the failure of the use of side rooms in general wards to control transmission may have been related to diminished effectiveness of hand hygiene in this situation. Recent studies by Sprivulis et al.1 and Richardson2 showed increased inpatient mortality when there is excessive bed occupancy but they do not explore the causes of this increased mortality. In addition, the study by Liew et al.3 does not examine the causes of increased length of stay. Further work is needed to provide this information. However, it seems probable that increased hospital-acquired infections may be one of the major causes.5,6 Furthermore, access block in isolation wards may be one of the consequences of excessive bed occupancy and, by enhancing transmission, also one of its causes. It seems probable that the short-term efficiency gained by running hospitals at near 100% capacity, as is currently common in Australia, may in fact increase costs overall. Since there is considerable ‘re-work’ because of increased numbers of adverse events that result in longer lengths of stay, the increased efficiency of continued high bed occupancy may well be an illusion. Further development of the simple analyses described here may enable administrators to estimate the minimum number of isolation beds that a hospital requires. In addition, they may provide a mechanism for determining the number of cohorting beds that may be required at any time, for example when multiple antibiotic-resistant organism numbers increase. The use of an approximate upper control limit based on the 97.725th percentile (equivalent to two standard deviations) of the Poisson distribution is somewhat empirical and requires further study. However, with the new isolate data, a limit based on the standard deviation of the 110 residuals (the differences between the values predicted by the model and the observed values) with three outliers excluded, leaving 107 normally distributed residuals also identified the three outlier groups labelled A, B and C in Figure 3. Outlier group A has been reported elsewhere.16 Outlier group B is not prominent in Figure 1 but the corresponding occupied bed days were reduced due to the hospital relocating.
50
A. Morton et al.
Outlier group C was the culmination of a significant upward trend that is clearly shown in Figure 1.
References 1.
Sprivulis P, Da Silva A, Jacobs I, Frazer A, Jelinek G. The association between hospital overcrowding and mortality among patients admitted via Western Australian emergency departments. Med J Aust 2006; 184: 208–12.
2.
Richardson D. Increase in patient mortality at 10 days associated with emergency department overcrowding. Med J Aust 2006; 184: 213–16.
3.
Liew D, Liew D, Kennedy M. Emergency department length of stay independently predicts excess inpatient length of stay. Med J Aust 2003; 179: 524–6.
4.
Cameron P. Hospital overcrowding: a threat to patient safety? Med J Aust 2006; 184: 203–4.
5.
Cunningham J, Kernohan W, Sowney R. Bed occupancy and turnover interval as determinant factors in MRSA infections in acute settings in Northern Ireland. J Hosp Infect 2005; 61: 189–93. doi:10.1016/ j.jhin.2005.04.014
6.
Cunningham J, Kernohan W, Rush T. Bed occupancy, turnover intervals and MRSA rates in English hospitals. Br J Nurs 2006; 15: 656–60.
7.
McCannon M, Hackbarth A, Griffin F. Miles to go: an introduction to the 5 million lives campaign. Jt Comm J Qual Patient Saf 2007; 33: 477–84.
8.
Ferguson J. A call to arms. Aust Infect Control 2007; 12: 39–40.
9.
Duckworth G. Controlling methicillin resistant Staphylococcus aureus. BMJ 2003; 327: 1177–8. doi:10.1136/bmj.327.7425.1177
10. Collignon P, Grayson L, Johnson P. Methicillin-resistant Staphylococcus aureus in hospitals: time for a culture change. Med J Aust 2007; 187: 4–5. 11. Beggs C, Noakes C, Shepherd S, Kerr K, Sleigh A, Banfield K. The influence of nurse cohorting on hand hygiene effectiveness. Am J Infect Control 2006; 34: 621–6. doi:10.1016/j.ajic.2006.06.011 12. Ferguson J. Healthcare-associated methicillin-resistant Staphylococcus aureus (MRSA) control in Australia and New Zealand. Aust Infect Control 2007; 12: 60–6. 13. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2008. Available online at: http://www.R-project.org [accessed February 2009]. 14. Wood S. Generalized additive models. Chapman & Hall/CRC: Boca Raton; 2006. 15. Morton A, Gatton M, Tong E, Clements A. New control chart methods for monitoring MROs in hospitals. Aust Infect Control 2007; 12: 14–18. 16. Bartley P, Schooneveldt J, Looke D, Morton A, Johnson D, Nimmo G. The relationship of a clonal outbreak of Enterococcus faecium vanA to methicillin-resistant Staphylococcus aureus incidence in an Australian hospital. J Hosp Infect 2001; 48: 43–54. doi:10.1053/jhin.2000.0915 17. Centers for Disease Control and Prevention. Guideline for Hand Hygiene in Healthcare Settings – 2002. Atlanta, GA: Centers for Disease Control and Prevention; 2002. Available online at: http:// www.cdc.gov/handhygiene/ [verified April 2009]. 18. Bagust A, Place M, Posnett J. Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. BMJ 1999; 319: 155–8.