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Critical Care

, 11:154 | Cite as

Staffing level: a determinant of late-onset ventilator-associated pneumonia

Commentary

Abstract

A body of knowledge exists to suggest an association between nurse staffing and adverse patient outcomes. Hugonnet and colleagues add further evidence by linking nurse staffing to late-onset ventilator-associated pneumonia. Discussed are a number of concerns surrounding the analytic component of this study, including the construction of variables and the statistical models. The authors' estimation that hospitals maintaining a nurse-to-patient ratio above 2.2 could decrease the risk of health care associated infections is based on findings that are potentially biased and unrealistic.

Keywords

Nurse Staffing Hand Hygiene Risk Factor Analysis Risk Adjustment Model Adequate Staffing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

VAP

ventilator-associated pneumonia.

Hugonnet and colleagues [1] present an interesting article on nurse staffing and ventilator-associated pneumonia (VAP). Although this study joins a number of other studies on nurse staffing and adverse outcomes of hospitalized patients, I feel compelled to address several important limitations of this study. What I find disappointing is the fact that these authors describe an observational study in which I expect they had an opportunity to add something substantial to the body of literature on nurse staffing and adverse health care related outcomes, but failed to do so.

There is sufficient evidence in the literature to suggest that nurse staffing is significantly associated with health care associated infections [2, 3, 4], but we lack data on the process of nursing care that may very well inform us as to why this staffing association exists. The authors do state that some of the process of care measures were not consistently recorded, but they do not state that all of those measures were missing. Because these data on the process of care are of such importance, I would hope that the authors considered a method of imputation before making the decision to eliminate these data from the analysis. They control for central venous, peripheral, and urinary catheters, but it would have been of great value to include data on nurse process of care measures, such as the presence or absence of mouth care [5, 6], which is a potential risk factor for VAP. In addition, I am surprised that the authors did not include hand hygiene as a risk factor of interest, because there is a well established link between hand hygiene and health care associated infections, and one that the authors have worked with extensively [7, 8].

The authors painstakingly constructed a comprehensive risk adjustment model that includes, but is not limited, to the Charlson comorbidity index [9], the Acute Physiology and Chronic Health Evaluation II score [10], and the Projet de Recherché en Nursing acuity score. I am concerned, however, that these measures overlap and, although not mentioned, I hope that the authors verified that there were no issues with collinearity. The Cox hazards model is an appropriate choice when measuring time to VAP, but I have a few concerns surrounding the method of censoring and construction of the variables that are time dependent. I can understand the exposure period for the nurse staffing variable, but I think it best to construct all other of the time-dependent variables as days from admission to censoring. As for censoring, I also do not agree with censoring 5 days post-extubation. The authors' choice of censoring prohibits taking into account the patients who might well have experienced respiratory compromise and required re-intubation. Because the extubated patients were censored (removed from the analytic model) on day 5 after extubation, these patients are no longer included in the sample for analytic purposes, even though they are still presumably at risk for VAP.

What I found most troublesome with this analysis is how the authors computed what they refer to as the risk factor of interest, namely nurse staffing. They refer in the text to the nurse staffing per shift, and in fact they provide the median nurse-to-patient ratio for the morning, evening, and night shifts as 0.8, 0.6, and 0.6, respectively. However, in the final hazard models nurse staffing is computed as the total number of nurses working in a 24-hour day divided by the patient census. Such a computation inflates the nurse-to-patient ratio, as indicated by the fact the median daily ratio ranged from 1.4 to 5.3 nurses per patient. Nurse staffing has been computed differently in a number of studies in the literature, such as full-time equivalent registered nurses per adjusted inpatient day [11], registered nurse hours per adjusted inpatient day [12], and nurse-to-patient ratio [13]; although these computations differ, the final recommendations make some sense from an administrative point of view. The estimation by the authors that hospitals maintaining a nurse-to-patient ratio above 2.2 could decrease the risk of health care associated infections is based on findings that are potentially biased and unrealistic.

Even though Hugonnet and colleagues provide what I consider to be suboptimal estimates of nurse-to-patient ratio, I applaud their attempt to forge along the causal pathway that links nurse staffing to health care associated infections in an attempt to improve the quality of patient care.

Authors' response

Stéphane Hugonnet

We thank Cimiotti for her detailed commentary on our study [1] and focus on the few relevant criticisms she makes in our response.

Although knowledge in this field is still partial and further research is required [14], our study adds to the increasing evidence in the literature that adequate staffing is a prerequisite for patient safety. It provides additional data on the epidemiology of VAP; few studies have investigated the association between workload and pneumonia [15, 16], and none have specifically focused on late-onset VAP.

The optimal method with which to estimate how much time and care each patient received in order to derive some sort of an 'offer/demand' ratio would be to measure it individually, but this is unrealistic. Computing a workload measure per shift or over 24 hours does not make any difference, as explained in our report. Neither is there any fundamental difference between measuring nurse-to-patient ratio, full-time equivalent nurses, or number of nurse hours per patient. These details should not blur what is by far the main problem; these measures are all of an ecological nature [3, 12, 17] and this is seldom acknowledged.

We agree with Cimiotti that the risk factor analysis for VAP is not straightforward. Because we investigated only VAP, the analysis of time or time at risk cannot start before initiation of mechanical ventilation, and precisely how long a patient remains at risk after extubation is unknown. We agree that 5 days is an arbitrary cut-off value, but it seems very reasonable to assume that a pneumonia developing 7 days after extubation is unrelated to mechanical ventilation, as long as there is no intervening re-intubation. Of note, a patient who was extubated and re-intubated 3 days later was still in the at-risk period and included in the analysis.

We agree that the process of care is an important issue, but lies in the causal pathway between workload and infection. However, the priority is surely not to demonstrate that busy health care workers do not fully comply with infection control recommendations, but rather to improve the process of care, define adequate staffing levels, and refine statistical and mathematical techniques in risk factor analysis [14, 17].

Notes

References

  1. 1.
    Hugonnet S, Uckay I, Pittet D: Staffing level: a determinant of late-onset ventilator-associated pneumonia. Crit Care 2007, 11: R80. 10.1186/cc5974PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    Alonso-Echanove J, Edwards JR, Richards MJ, Brennan P, Venezia RA, Keen J, Ashline V, Kirkland K, Chou E, Hupert M, et al.: Effect of nurse staffing and antimicrobial-impregnated central venous catheters on the risk for bloodstream infections in intensive care units. Infect Control Hosp Epidemiol 2003, 24: 916-925. 10.1086/502160CrossRefPubMedGoogle Scholar
  3. 3.
    Cimiotti JP, Haas J, Saiman L, Larson EL: Impact of staffing on bloodstream infections in the neonatal intensive care unit. Arch Pediatr Adolesc Med 2006, 160: 832-836. 10.1001/archpedi.160.8.832CrossRefPubMedGoogle Scholar
  4. 4.
    Harbarth S, Sudre P, Sudre , Dharan S, Cadenas M, Pittet D: Outbreak of Enterobacter cloacae related to understaffing, overcrowding, and poor hygiene practices. Infect Control Hosp Epidemiol 1999, 20: 598-603. 10.1086/501677CrossRefPubMedGoogle Scholar
  5. 5.
    Fourrier F, Duvivier B, Boutigny H, Roussel-Delvallez M, Chopin C: Colonization of dental plaque: a source of nosocomial infections in intensive care unit patients. Crit Care Med 1998, 26: 301-308. 10.1097/00003246-199802000-00032CrossRefPubMedGoogle Scholar
  6. 6.
    Mori H, Hirasawa H, Oda S, Shiga H, Matsuda K, Nakamura M: Oral care reduces incidence of ventilator-associated pneumonia in ICU populations. Intensive Care Med 2006, 32: 230-236. 10.1007/s00134-005-0014-4CrossRefPubMedGoogle Scholar
  7. 7.
    Pittet D, Dharan S, Touveneau S, Sauvan V, Perneger TV: Bacterial contamination of the hands of hospital staff during routine patient care. Arch Intern Med 1999, 159: 821-826. 10.1001/archinte.159.8.821CrossRefPubMedGoogle Scholar
  8. 8.
    Pittet D, Kramer A, Hugonnet S: Alcohol-based hand gels and hand hygiene in hospitals. Hand hygiene revisited: lessons from the past and present. Lancet 2002, 360: 1511. 10.1016/S0140-6736(02)11447-4CrossRefGoogle Scholar
  9. 9.
    Charlson ME, Pompei P, Ales KL, MacKenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987, 40: 373-383. 10.1016/0021-9681(87)90171-8CrossRefPubMedGoogle Scholar
  10. 10.
    Knaus WA, Draper EA, Wagner DP, Zimmerman JE: APACHE II: a severity of disease classification system. Crit Care Med 1985, 13: 818-829. 10.1097/00003246-198510000-00009CrossRefPubMedGoogle Scholar
  11. 11.
    Kovner C, Gergen PJ: Nurse staffing levels and adverse events following surgery in U.S. hospitals. Image J Nurs Sch 1998, 30: 315-321.CrossRefPubMedGoogle Scholar
  12. 12.
    Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K: Nurse-staffing levels and the quality of care in hospitals. N Engl J Med 2002, 346: 1715-1722. 10.1056/NEJMsa012247CrossRefPubMedGoogle Scholar
  13. 13.
    Aiken LH, Clarke SP, Sloane DM, Sochalski J, Silber JH: Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA 2002, 288: 1987-1993. 10.1001/jama.288.16.1987CrossRefPubMedGoogle Scholar
  14. 14.
    Assadian O, Toma CD, Rowley SD: Implications of staffing ratios and workload limitations on healthcare-associated infections and the quality of patient care. Crit Care Med 2007, 35: 296-298. 10.1097/01.CCM.0000251291.65097.8ACrossRefPubMedGoogle Scholar
  15. 15.
    Amaravadi RK, Dimick JB, Pronovost PJ, Lipsett PA: ICU nurse-to-patient ratio is associated with complications and resource use after esophagectomy. Intensive Care Med 2000, 26: 1857-1862. 10.1007/s001340000720CrossRefPubMedGoogle Scholar
  16. 16.
    Dimick JB, Swoboda SM, Pronovost PJ, Lipsett PA: Effect of nurse-to-patient ratio in the intensive care unit on pulmonary complications and resource use after hepatectomy. Am J Crit Care 2001, 10: 376-382.PubMedGoogle Scholar
  17. 17.
    Grundmann H, Hori S, Winter B, Tami A, Austin D: Risk factors for the transmission of methicillin-resistant Staphylococcus aureus in an adult intensive care unit: fitting a model to the data. J Infect Dis 2002, 185: 481-488. 10.1086/338568CrossRefPubMedGoogle Scholar

Copyright information

© BioMed Central Ltd 2007

Authors and Affiliations

  1. 1.Center for Health Outcomes and Policy ResearchUniversity of Pennsylvania School of NursingPhiladelphiaUSA

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