Skip to main content
Download PDF

Hand hygiene improvement of individual healthcare workers: results of the multicentre PROHIBIT study

Antimicrobial Resistance & Infection Control volume 11, Article number: 123 (2022) Cite this article

A Correction to this article was published on 19 February 2023

This article has been updated

Abstract

Background

Traditionally, hand hygiene (HH) interventions do not identify the observed healthcare workers (HWCs) and therefore, reflect HH compliance only at population level. Intensive care units (ICUs) in seven European hospitals participating in the “Prevention of Hospital Infections by Intervention and Training” (PROHIBIT) study provided individual HH compliance levels. We analysed these to understand the determinants and dynamics of individual change in relation to the overall intervention effect.

Methods

We included HCWs who contributed at least two observation sessions before and after intervention. Improving, non-changing, and worsening HCWs were defined with a threshold of 20% compliance change. We used multivariable linear regression and spearman’s rank correlation to estimate determinants for the individual response to the intervention and correlation to overall change. Swarm graphs visualized ICU-specific patterns.

Results

In total 280 HCWs contributed 17,748 HH opportunities during 2677 observation sessions. Overall, pooled HH compliance increased from 43.1 to 58.7%. The proportion of improving HCWs ranged from 33 to 95% among ICUs. The median HH increase per improving HCW ranged from 16 to 34 percentage points. ICU wide improvement correlated significantly with both the proportion of improving HCWs (ρ = 0.82 [95% CI 0.18–0.97], and their median HH increase (ρ = 0.79 [0.08–0.97]). Multilevel regression demonstrated that individual improvement was significantly associated with nurse profession, lower activity index, higher nurse-to-patient ratio, and lower baseline compliance.

Conclusions

Both the proportion of improving HCWs and their median individual improvement differed substantially among ICUs but correlated with the ICUs’ overall HH improvement. With comparable overall means the range in individual HH varied considerably between some hospitals, implying different transmission risks. Greater insight into improvement dynamics might help to design more effective HH interventions in the future.

Introduction

Healthcare-associated infections (HAIs) affect on average 6% of hospitalized patients in Europe [1, 2]. Patients in hospitals are at increased risk of acquiring HAIs mainly because of invasive procedures. The proximity to other patients and frequent healthcare contacts facilitate the transmission of pathogens, and the use of broad-spectrum antibiotics increases the burden of multidrug-resistant microorganisms (MDROs) in hospital settings [3]. Hand hygiene (HH) is the most basic and essential element in the prevention of cross-transmission. Although widely promoted, HH compliance in intensive care units (ICU) remains on average 40–50% [4,5,6,7], and to sustain achieved improvements remains challenging [8,9,10,11].

All but a few small studies [12,13,14,15,16,17] on HH compliance report only pooled data across all healthcare workers (HCW). Thus there is little data on the contribution of individuals to the overall response to behavioural interventions. Individual HH compliance is relevant because average compliance does not reflect variations among HCWs and thus, might not capture overall transmission risk. Moreover, data on changes in individual HH compliance could provide insights into barriers and facilitators of a HH intervention. In the Prevention of Hospital Infections by Intervention and Training (PROHIBIT) intervention study, HH compliance was observed at the individual HCW level during baseline and intervention study periods. We analysed these data to better understand the determinants and dynamics of individual change in HH compliance in relation to the overall intervention effect.

Methods

Methods of the PROHIBIT intervention study

The PROHIBIT intervention study tested two interventions to prevent central venous catheter bloodstream infection (CRBSI) in Intensive Care Units (ICUs) in European acute care hospitals: a central venous catheter (CVC) insertion strategy, and a HH improvement strategy. Several other work packages addressed the topic of HAI prevention more widely [18,19,20,21,22]. After a baseline period of at least six months, three ICUs of 14 hospitals in 11 European countries were randomly allocated every three months to start with one of the two intervention strategies or both [7]. The method and results of this step wedge randomised controlled intervention study have been reported in detail elsewhere [7, 23].

In brief, each ICU appointed one dedicated on-site investigator and one study nurse to the project. PROHIBIT offered reimbursement of a 0.5 full-time equivalent study nurse. Three to six months before the start of the intervention, local study nurses and/or infection control physicians, anaesthetists, and intensivists, depending on the intervention, attended a two-day PROHIBIT workshop on best practices and implementation science. For the HH improvement strategy, PROHIBIT used the WHO HH training and campaign materials (http://www.who.int/gpsc/5may/tools/en/). Hand hygiene promotion included educational sessions and bedside training. In addition, the ICUs displayed posters and/or other reminders in the workplace and participants came up with various additional promotion activities. Hand hygiene compliance was measured by direct observation according to the WHO observation method [24]. PROHIBIT study nurses, most often infection prevention and control (IPC) or ICU nurses with IPC responsibilities, were trained in the methodology of direct HH observation at the University of Geneva Hospitals, Switzerland. Hand hygiene observations were randomized for date (weekdays), time slot (08–12:00, 12:00–16:00 and 16:00–20:00), and ICU bed [7]. One observation session could include observations of multiple HCWs. However, to avoid missing HH opportunities, observers were not allowed to observe more than three HCWs in one session. Hand hygiene opportunities were stratified by observation sessions and by the five WHO indications for HH [24]. HH compliance was calculated as the proportion of HH opportunities met by a HH action. Individual HCWs identity was recorded using a four-letter code, based on their given and family name, where needed retrieved from the badge and, if that proved impossible, by asking the HCW.

During the intervention on site investigators received quarterly feedback reports on the average HH compliance in their ICU and individual HCWs on their HH compliance after being observed, but not during baseline [7]. HCW codes were used for statistical analysis only.

Study population of the present analysis

Ten of the 14 ICUs agreed to capture the HCWs’ identity during HH observations. Seven of these ICUs implemented the HH intervention, either alone or in combination with the CRBSI-prevention strategy and were included in the present analysis as study ICUs. Only data of HCWs with at least two observation sessions, during both baseline and intervention periods, were included for the individual analysis (‘study HCWs’).

Definitions

HCWs were grouped according to the change in their HH compliance between baseline and intervention.

‘Improving HCW’ were defined by having improved compliance by at least 20%, ‘Worsening HCW’ decreased by at least 20%, and ‘Non-changing HCW’ changed less than 20%, if at all. The 20%-threshold was chosen retrospectively based on the rounded pooled mean change among all HCWs. An ‚activity index ‘, defined by number of hand hygiene opportunities per hour of observation, was defined as a proxy for the intensity of care [25].

Analysis plan and statistics

To meet our study scope, we chose four analytical models.

In Model 1, we evaluated the extent to which changes in the individual HH compliance between study periods were associated with ICU characteristics. We calculated Spearman rank correlation coefficients of the proportion of Improving HCWs with a) the nurse-to-patient ratio and b) the pooled mean baseline HH compliance of all HCWs.

In Model 2, we assessed a potential association between the intervention effect on the individual HCW and the overall ICU. We calculated the Spearman’ rank correlation coefficients for the proportion and median improvement of Improving HCWs with the pooled change in HH compliance of all HCWs.

In Model 3, we tested the potential association of the change in HH compliance for each individual HCW (measured as change in percentage points (pp); outcome variable) with HCW characteristics (i.e., professional category, baseline compliance) and contextual factors (i.e., activity index, ICU type, ICU nurse- to-patient ratio, and proportion of improving HCWs). We used a generalized linear mixed model (GLMM) with a normal distribution, allowing for clustering at the ICU level. Variables with a P value < 0.25 in the univariable analysis were included in the multivariable model using manual backward selection. The proportion of explained variation (R2) was calculated for this model.

All changes in HH compliance were calculated as relative proportions (%) or differences in percentage points (pp), using mid-P exact tests to test for significance. We used SAS 9.4 (SAS Institute Inc., Cary, United States) for all statistical analyses.

In Model 4, we created a swarm plot with individual HH compliance at baseline and intervention for each HCW, in each category (improving, non-improving, worsening HCWs), and a bar diagram of the range in HH between HH sessions for each HCW, during the baseline and intervention phase, for visual display of individual HCW compliance patterns in the seven study ICUs.

Results

Study intensive care units and healthcare worker population

Three of the seven study hospitals were university affiliated, two had > 50,000 admissions per year, two between 30,000 and 50,000, and three < 30,000 admissions per year. The median number of ICU beds per hospital was 17 (range 10–40). The median nurse-to-patient ratio in the ICU during day shifts was 0.5 (range 0.29–1.00; Additional file 1: Table S1) at baseline, i.e., one nurse for two patients. The median activity index in the ICUs was 9.0 (IQR, 6.0–15.0) HH opportunities per hour. According to the inclusion criteria, 280 study HCWs (58% nurses, 20% doctors, 18% auxiliary nurses, 4% other HCWs) contributed 17,748 HH opportunities during 2677 observation sessions with a median number of sessions, respectively opportunities, per HCW of 4 [interquartile range (IQR), 2–6], respectively 15 (IQR 10–23), during baseline and 10 (IQR 5–15), respectively 39 (IQR 18–58.5), during intervention. During baseline 365 HCW and during intervention another 623 HCWs were excluded because they did not meet the inclusion criteria of at least two observation sessions per study period.

Hand hygiene compliance

The pooled mean HH compliance of study HCWs increased significantly from 43.1% during baseline to 58.7% during intervention (Table 1). Similarly, the compliance of the 365 excluded HCWs was 43.1% during baseline. Overall HH compliance of study HCWs and non-study HCWs increased from 43.1% to 60.8% (Additional file 1: Table S2, 61.0% for the excluded 623 HCWs). For Improving HCWs, Worsening HCWs, and Non-changing HCWs HH compliance changed from 35 to 57%, 50% to 41%, and 59% to 66%, respectively. Individual HH compliance per HCW for the entire study population is shown in Fig. 1 and for each study ICU in Additional file 1: Figs. S1 and S2.

Table 1 Hand hygiene compliance per hospital during baseline and intervention
Full size table
Fig. 1
figure 1

Individual change of hand hygiene compliance, stratified by intensive care unit. Each line represents one HCW. Impr., improving healthcare workers; NC, Non-changing healthcare workers; Wors., Worsening healthcare workers

Full size image

Model 1: The proportion of Improving Healthcare workers and associated factors per ICU

The overall proportion of Improving HCWs was 62.1% with an inter-ICU range of 32.7% to 95.2% (Table 1). Per ICU, the proportion of Improving HCWs was not significantly associated with the nurse-to-patient ratio (ρ 0.23; CI − 0.64 to 0.84) or the overall baseline compliance (ρ 0.37; − 0.86 to 0.54).

The average improvement in HH compliance was negatively associated with the activity index (Additional file 1: Table S1).

Model 2: Association of the intervention effect between individual HCWs and the overall ICU

The median increase in HH compliance of Improving HCWs per ICU ranged from 16 to 34 pp (Additional file 1: Fig. S2) and was significantly associated with the overall improvement among all HCWs in the corresponding ICU (ρ 0.79; CI 0.08–0.97) (Fig. 2). The overall proportion of Improving HCWs was 62.1% with an inter-ICU range of 32.7% to 95.2% (Table 1). This proportion of Improving HCWs per ICU was associated with the overall HH improvement among all HCWs [Spearman rank correlation (ρ) 0.82; 95% confidence interval (CI), 0.18–0.97] (Table 1, Fig. 3).

Fig. 2
figure 2

Correlation between median hand hygiene improvement of improving healthcare workers and overall improvement in hand hygiene compliance per hospital. HH, hand hygiene; pp, percentage points

Full size image
Fig. 3
figure 3

Correlation between the proportions of improving healthcare workers and the overall improvement in hand hygiene compliance per hospital. HH, hand hygiene; pp, percentage points

Full size image

Model 3: Factors associated with individual HH compliance change between study periods

In multivariable analysis, a higher nurse-to-patient ratio was positively associated, and professional category ‘medical doctor/ students’, higher activity index, and higher individual baseline HH compliance were negatively associated with individual changes in HH compliance between study periods (Table 2). The multivariable regression model explained 43% of the variance in individual changes in HH compliance between study periods (marginal R2), 22% due to the independent variables (fixed effects) and 21% due to nested clustering on the HCWs and hospital level (random effects).

Table 2 Univariable and multivariable estimates of the effect on the change of hand hygiene compliance
Full size table

Model 4

Although lower baseline compliance overall was associated with a larger increase in HH compliance, it can seen in Fig. 1 that Improving HCWs with low baseline HH compliance remained lower in intervention compared to Improving HCWs who were already high during baseline. Figure 1 shows different HCW compliance patterns and changes across the study ICUs. In some centres the variability in HH compliance among individual HCWs increased from baseline to intervention – resulting from higher variability in compliance between HCWs (Additional file 1: Fig. S1) and/or between sessions of individual HCWs (Additional file 1: Fig. S3).

Discussion

The goal of this study was to understand the determinants and dynamics of individual HH compliance in response to an intervention. The intervention was a success, HH compliance increased significantly and importantly, overall and in each ICU. Individual compliance change was positively correlated with the overall change per ICU. However, we found large inter-ICU and inter-individual differences in the observed HH compliance and their dynamics. In some ICUs the overall result of the intervention was produced by almost exclusively improving HCWs, while in other ICUs the contribution of improving HCWs was to some extent offset by worsening or non-changing HCWs. We also found that individual compliance change was positively associated with a higher nurse-to-patient ratio, and negatively associated with a higher activity index, with being a physician or medical student, and higher baseline HH compliance.

This study is important, because understanding the patterns and dynamics of individual HH compliance in response to an intervention might help to better tailor improvement efforts. When individual responses to the intervention diverge strongly one might suspect factors that concern HCWs individually. In the contrary case, where all HCWs respond with a similar but smaller increase in HH compliance, contributing to the same overall result, systemic factors might be sought and addressed. For example, in addition to a high baseline compliance, these might include a flawed handrub dispenser placement, a high activity index, or a low nurse-to-patient ratio. Indeed, a higher nurse-to-patient ratio was independently associated with a higher HH improvement in this study. Moreover, the ICU with the highest nurse-to-patient ratio (ICU E) was the only one in which the activity index was not negatively associated with HH compliance. This suggests that the nurse-to-patient ratio is a relevant variable to target as a risk for low HH compliance. The negative effect of a high workload on HH compliance has been reported before [25,26,27,28]. Scheithauer et al. demonstrated an inverse relationship between the daily workload and HH [29], and Lee et al. found a positive association between nurse-to-patient ratio and HH compliance [16]. Data collected by Hansen et al. demonstrate a nurse to patient ratio of 0.5 during dayshifts to be typical for European ICUs, with national averages ranging from 0.3 to 0.9 [30].

Our study reveals considerable variability in HH compliance between individual HCWs, in some ICUs more than in others. The few previous studies that have employed individualized observation found similar between-HCW variability [12,13,14,15,16,17, 31]. However, these were either based on a small number of HCWs or did not perform an intervention. Estimating from Fig. 1 and Additional file 1: Fig. S1, this between-HCW variability seems to increase in some centres, suggesting differences in the individual response to the intervention. Personal perceptions, mental models, motivation, and work organisation can influence the individual HH behaviour of each HCW and, in consequence, also their response to promotional exposure [31,32,33,34]. Early recognition of these individual factors could help in customizing HH improvement strategies to a range of typical behavioural profiles. Methods from psychology and implementation science may be helpful to tailor improvement strategies to prospectively identified determinants of HH [35,36,37].

Data on the level of HH compliance needed to prevent cross-transmission are limited to modelling studies. Three reports identified a “threshold” of mean HH compliance above which pathogen transmission and infections would start to decline to be 48%, 66% and 87%, respectively, always assuming that each HH action results in a total eradication of pathogen transmission[38,39,40]. Models taking into account less than 100% efficacy, conclude that no level of HH can be identified as “good enough” to prevent transmission[41,42,43]. Most importantly, models have demonstrated that the distribution of HH compliance among HCWs in a population affects the ensuing transmission risk. A single HCW with low HH compliance could play a significant role in pathogen transmission, especially if such a low-performing HCW provides care to many patients consecutively. This effect was demonstrated in two agent-based models by Temime et al. and by Hornbeck et al. [44, 45]. Exemplarily, the proportion of low-performing staff in our study were predominantly doctors who typically deliver care to many different patients. This ‘weakest link’ mechanism challenges the usefulness of pooled means of HH compliance infectious risk in each care unit. Our real-world data support the idea that similar pooled mean HH compliance rates between observed settings can be the result of quite different distributions of high- and low-performing HCWs.

This study has limitations. First, not all observed HCWs could be included in the analysis due to the required number of observations. This could have led to an overrepresentation of permanent staff. Both the pooled baseline and intervention compliance were, however, comparable between the group of study HCWs and the excluded HCWs.

Second, the definition of Improving HCWs as those with ≥ 20% HH improvement precluded the inclusion of HCWs with > 80% compliance at baseline in this category. To circumvent this problem, we evaluated the possibility of using the change in non-compliance, rather than compliance to distinguish HCWs. This resulted in slightly higher correlations and similar effect estimates in the univariable regression model. We therefore decided to remain with the traditional definition of HH compliance. Third, the chosen cut-off value of 20% to distinguish HCWs into the three HH compliance change categories was somewhat arbitrary. However, a multivariable model with a cut-off value of 10% provided similar results (data not shown). Fourth, some of the observed between-HCW variability could be explained by chance due to a limited number of HH observation sessions per HCW and a limited number of opportunities per session. However, our study is the largest of its kind to date and demonstrates the feasibility and benefit of this approach. It might take an advanced automatic HH monitoring system to collect a larger number of opportunities per identified HCW. Finally, like other HH observation studies, observer and observation biases cannot be entirely excluded, especially a desirability bias by the observers also being involved in the promotion of the intervention, and observation bias, also known as Hawthorne effect [46,47,48]. However, given the long study duration of 30 months and the focus on improvement dynamics, it is likely that neither biases influenced the results to a degree that would invalidate our findings.

Conclusions

Both the proportion of improving HCWs and the median of individual HH improvement differed substantially among hospitals. Both measures were associated with the overall success of the intervention. However, the patterns and dynamics of individual HH compliance varied considerably among ICUs, and could potentially result in different risk of pathogen transmission. Being a nurse, a low individual baseline HH compliance, a lower ICU-level activity index, and a favourable nurse-to-patient ratio were associated with a higher individual HH compliance improvement. Data on individual HH compliance could advance our understanding of improvement dynamics and inform better intervention strategies. Collecting individual level HH data should be seriously considered in future HH research, especially in the design of interventions.

Availability of data and materials

The data are stored with the RIVM. Applications to use the data for further research can be sent to Dr. Walter Zingg (e-mail, Walter.Zingg@usz.ch).

Change history

Abbreviations

ABHR:

Alcohol-based handrub

CRBSI:

Catheter-related bloodstream infection

CVC:

Central venous catheter

HAI:

Health care-associated infection

HCW:

Healthcare worker

HH:

Hand hygiene

ICU:

Intensive care unit

IPC:

Infection prevention and control

IQR:

Interquartile range

MDRO:

Multidrug-resistant microorganism

pp:

Percentage point

PROHIBIT:

Prevention of hospital infection by intervention and training

WHO:

World Health Organization

References

  1. Suetens C, Latour K, Karki T, Ricchizzi E, Kinross P, Moro ML, et al. Prevalence of healthcare-associated infections, estimated incidence and composite antimicrobial resistance index in acute care hospitals and long-term care facilities: results from two European point prevalence surveys, 2016 to 2017. Euro Surveill. 2018;23(46):1800516.

  2. ECDC. Point prevalence survey of healthcare-associated infections and antimicrobial use in European acute care hospitals. Stockholm: European Centre for Disease Prevention and Control; 2013.

    Google Scholar 

  3. Versporten A, Zarb P, Caniaux I, Gros MF, Drapier N, Miller M, et al. Antimicrobial consumption and resistance in adult hospital inpatients in 53 countries: results of an internet-based global point prevalence survey. Lancet Glob Health. 2018;6(6):e619–29.

    Article  PubMed  Google Scholar 

  4. Moro ML, Morsillo F, Nascetti S, Parenti M, Allegranzi B, Pompa MG, et al. Determinants of success and sustainability of the WHO multimodal hand hygiene promotion campaign, Italy, 2007–2008 and 2014. Euro Surveill. 2017;22(23):30546.

  5. Derde LPG, Cooper BS, Goossens H, Malhotra-Kumar S, Willems RJL, Gniadkowski M, et al. Interventions to reduce colonisation and transmission of antimicrobial-resistant bacteria in intensive care units: an interrupted time series study and cluster randomised trial. Lancet Infect Dis. 2014;14(1):31–9.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Erasmus V, Daha TJ, Brug H, Richardus JH, Behrendt MD, Vos MC, et al. Systematic review of studies on compliance with hand hygiene guidelines in hospital care. Infect Control Hosp Epidemiol. 2010;31(3):283–94.

    Article  PubMed  Google Scholar 

  7. van der Kooi T, Sax H, Pittet D, van Dissel J, van Benthem B, Walder B, et al. Prevention of hospital infections by intervention and training (PROHIBIT): results of a pan-European cluster-randomized multicentre study to reduce central venous catheter-related bloodstream infections. Intensive Care Med. 2018;44(1):48–60.

    Article  PubMed  Google Scholar 

  8. Gould DJ, Chudleigh JH, Moralejo D, Drey N. Interventions to improve hand hygiene compliance in patient care. Cochrane Database Syst Rev. 2007;2:CD005186.

    Google Scholar 

  9. Schwadtke L, Graf K, Lutze B, von Lengerke T, Chaberny IF. Compliance with hand hygiene guidelines on intensive care units at a university medical centre with surgical focus. Dtsch Med Wochenschr. 2014;139(25–26):1341–5.

    CAS  PubMed  Google Scholar 

  10. Luangasanatip N, Hongsuwan M, Limmathurotsakul D, Lubell Y, Lee AS, Harbarth S, et al. Comparative efficacy of interventions to promote hand hygiene in hospital: systematic review and network meta-analysis. BMJ. 2015;351:h3728.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Fonguh S, Uwineza A, Catry B, Simon A. Belgian hand hygiene campaigns in ICU, 2005–2015. Arch Public Health. 2016;74:47.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Kerbaj J, Toure Y, Soto Aladro A, Boudjema S, Giorgi R, Dufour JC, et al. Smartphone text message service to foster hand hygiene compliance in health care workers. Am J Infect Control. 2017;45(3):234–9.

    Article  PubMed  Google Scholar 

  13. Sanchez-Carrillo LA, Rodriguez-Lopez JM, Galarza-Delgado DA, Baena-Trejo L, Padilla-Orozco M, Mendoza-Flores L, et al. Enhancement of hand hygiene compliance among health care workers from a hemodialysis unit using video-monitoring feedback. Am J Infect Control. 2016;44(8):868–72.

    Article  PubMed  Google Scholar 

  14. Cheng VC, Tai JW, Ho SK, Chan JF, Hung KN, Ho PL, et al. Introduction of an electronic monitoring system for monitoring compliance with Moments 1 and 4 of the WHO “My 5 Moments for Hand Hygiene” methodology. BMC Infect Dis. 2011;11:151.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kurtz SL. Identification of low, high, and super gelers and barriers to hand hygiene among intensive care unit nurses. Am J Infect Control. 2017:45(8):839–43.

  16. Lee A, Chalfine A, Daikos GL, Garilli S, Jovanovic B, Lemmen S, et al. Hand hygiene practices and adherence determinants in surgical wards across Europe and Israel: a multicenter observational study. Am J Infect Control. 2011;39(6):517–20.

    Article  PubMed  Google Scholar 

  17. Iversen AM, Kavalaris CP, Hansen R, Hansen MB, Alexander R, Kostadinov K, et al. Clinical experiences with a new system for automated hand hygiene monitoring: a prospective observational study. Am J Infect Control. 2020;48(5):527–33.

    Article  PubMed  Google Scholar 

  18. Martin M, Zingg W, Knoll E, Wilson C, Dettenkofer M, Group PS. National European guidelines for the prevention of Clostridium difficile infection: a systematic qualitative review. J Hosp Infect. 2014;87(4):212–9.

  19. Hansen S, Schwab F, Gastmeier P, group Ps, Pittet D, Zingg W, et al. Provision and consumption of alcohol-based hand rubs in European hospitals. Clin Microbiol Infect. 2015;21(12):1047–51.

  20. Hansen S, Schwab F, Gastmeier P, Zingg W, group Ps. Association of national and hospital factors to hospitals' alcohol-based handrub consumption in Europe: results of the European PROHIBIT study. Clin Microbiol Infect. 2018;24(7):778 e1-e6.

  21. Hansen S, Schwab F, Zingg W, Gastmeier P, The Prohibit Study G. Process and outcome indicators for infection control and prevention in European acute care hospitals in 2011 to 2012—Results of the PROHIBIT study. Euro Surveill. 2018;23(21):1700513.

  22. Hansen S, Zingg W, Ahmad R, Kyratsis Y, Behnke M, Schwab F, et al. Organization of infection control in European hospitals. J Hosp Infect. 2015;91(4):338–45.

    Article  CAS  PubMed  Google Scholar 

  23. Clack L, Zingg W, Saint S, Casillas A, Touveneau S, da Liberdade JF, et al. Implementing infection prevention practices across European hospitals: an in-depth qualitative assessment. BMJ Qual Saf. 2018;27(10):771–80.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sax H, Allegranzi B, Chraiti MN, Boyce J, Larson E, Pittet D. The World Health Organization hand hygiene observation method. Am J Infect Control. 2009;37(10):827–34.

    Article  PubMed  Google Scholar 

  25. Pittet D, Hugonnet S, Harbarth S, Mourouga P, Sauvan V, Touveneau S, et al. Effectiveness of a hospital-wide programme to improve compliance with hand hygiene. Infect Control Programme Lancet. 2000;356(9238):1307–12.

    CAS  Google Scholar 

  26. O’Boyle CA, Henly SJ, Larson E. Understanding adherence to hand hygiene recommendations: the theory of planned behavior. Am J Infect Control. 2001;29(6):352–60.

    Article  CAS  PubMed  Google Scholar 

  27. Sharma S, Sharma S, Puri S, Whig J. Hand hygiene compliance in the intensive care units of a tertiary care hospital. Indian J Commun Med. 2011;36(3):217–21.

    Article  Google Scholar 

  28. Stewardson AJ, Sax H, Gayet-Ageron A, Touveneau S, Longtin Y, Zingg W, et al. Enhanced performance feedback and patient participation to improve hand hygiene compliance of health-care workers in the setting of established multimodal promotion: a single-centre, cluster randomised controlled trial. Lancet Infect Dis. 2016;16(12):1345–55.

    Article  PubMed  Google Scholar 

  29. Scheithauer S, Batzer B, Dangel M, Passweg J, Widmer A. Workload even affects hand hygiene in a highly trained and well-staffed setting: a prospective 365/7/24 observational study. J Hosp Infect. 2017;97(1):11–6.

    Article  CAS  PubMed  Google Scholar 

  30. Hansen S, Schwab F, Behnke M, Carsauw H, Heczko P, Klavs I, et al. National influences on catheter-associated bloodstream infection rates: practices among national surveillance networks participating in the European HELICS project. J Hosp Infect. 2009;71(1):66–73.

    Article  CAS  PubMed  Google Scholar 

  31. Dufour JC, Reynier P, Boudjema S, Soto Aladro A, Giorgi R, Brouqui P. Evaluation of hand hygiene compliance and associated factors with a radio-frequency-identification-based real-time continuous automated monitoring system. J Hosp Infect. 2017;95(4):344–51.

    Article  PubMed  Google Scholar 

  32. Smiddy MP, O’Connell R, Creedon SA. Systematic qualitative literature review of health care workers’ compliance with hand hygiene guidelines. Am J Infect Control. 2015;43(3):269–74.

    Article  PubMed  Google Scholar 

  33. Sax H, Clack L. Mental models: a basic concept for human factors design in infection prevention. J Hosp Infect. 2015;89(4):335–9.

    Article  CAS  PubMed  Google Scholar 

  34. Lutze B, Chaberny IF, Graf K, Krauth C, Lange K, Schwadtke L, et al. Intensive care physicians’ and nurses’ perception that hand hygiene prevents pathogen transmission: belief strength and associations with other cognitive factors. J Health Psychol. 2017;22(1):89–100.

    Article  PubMed  Google Scholar 

  35. Grayson ML, Macesic N, Huang GK, Bond K, Fletcher J, Gilbert GL, et al. Use of an innovative personality-mindset profiling tool to guide culture-change strategies among different healthcare worker groups. PLoS ONE. 2015;10(10):e0140509.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Gould DJ, Navaie D, Purssell E, Drey NS, Creedon S. Changing the paradigm: messages for hand hygiene education and audit from cluster analysis. J Hosp Infect. 2018;98(4):345–51.

    Article  CAS  PubMed  Google Scholar 

  37. Sassenrath C, Diefenbacher S, Siegel A, Keller J. A person-oriented approach to hand hygiene behaviour: emotional empathy fosters hand hygiene practice. Psychol Health. 2016;31(2):205–27.

    Article  PubMed  Google Scholar 

  38. McBryde ES, Pettitt AN, McElwain DL. A stochastic mathematical model of methicillin resistant Staphylococcus aureus transmission in an intensive care unit: predicting the impact of interventions. J Theor Biol. 2007;245(3):470–81.

    Article  CAS  PubMed  Google Scholar 

  39. Grundmann H, Hori S, Winter B, Tami A, Austin DJ. 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(4):481–8.

    Article  PubMed  Google Scholar 

  40. Doan TN, Kong DC, Marshall C, Kirkpatrick CM, McBryde ES. Modeling the impact of interventions against Acinetobacter baumannii transmission in intensive care units. Virulence. 2016;7(2):141–52.

    Article  PubMed  Google Scholar 

  41. Goldstein ND, Eppes SC, Mackley A, Tuttle D, Paul DA. A network model of hand hygiene: how good is good enough to stop the spread of MRSA? Infect Control Hosp Epidemiol. 2017;38(8):945–52.

    Article  PubMed  Google Scholar 

  42. Greene C, Ceron NH, Eisenberg MC, Koopman J, Miller JD, Xi C, et al. Asymmetric transfer efficiencies between fomites and fingers: impact on model parameterization. Am J Infect Control. 2018;46(6):620–6.

    Article  PubMed  Google Scholar 

  43. D’Agata EM, Webb GF, Horn MA, Moellering RC Jr, Ruan S. Modeling the invasion of community-acquired methicillin-resistant Staphylococcus aureus into hospitals. Clin Infect Dis. 2009;48(3):274–84.

    Article  PubMed  Google Scholar 

  44. Temime L, Kardas-Sloma L, Opatowski L, Brun-Buisson C, Boëlle PGD. NosoSim: an agent-based model of nosocomial pathogens circulation in hospitals. Procedia Comput Sci. 2012;1:2245–52.

    Article  Google Scholar 

  45. Hornbeck T, Naylor D, Segre AM, Thomas G, Herman T, Polgreen PM. Using sensor networks to study the effect of peripatetic healthcare workers on the spread of hospital-associated infections. J Infect Dis. 2012;206(10):1549–57.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Srigley JA, Furness CD, Baker GR, Gardam M. Quantification of the Hawthorne effect in hand hygiene compliance monitoring using an electronic monitoring system: a retrospective cohort study. BMJ Qual Saf. 2014;23(12):974–80.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Dawson CH. Future systems of measurement for hand hygiene in healthcare. Warwick: University of Warwick; 2014.

    Google Scholar 

  48. Hagel S, Reischke J, Kesselmeier M, Winning J, Gastmeier P, Brunkhorst FM, et al. Quantifying the Hawthorne effect in hand hygiene compliance through comparing direct observation with automated hand hygiene monitoring. Infect Control Hosp Epidemiol. 2015;36(8):957–62.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

We thank Sylvie Touveneau, Marie-Noëlle Chraïti and Benedetta Allegranzi, who devoted themselves to the training of the on-site investigators and study nurses in the workshops. Our gratitude extends also to the participating centres for collaborating in this study, especially the management and staff of the centres that allowed the individual compliance results to be recorded and that responded positively to the challenge to improve their hand hygiene and thus contributed to the increased safety of the patients in their care.

Funding

The study was funded by the European Commission 7th Framework Programme.

Author information

Author notes

  1. Tjallie van der Kooi and Hugo Sax contributed equally to the work

Authors and Affiliations

  1. RIVM National Institute for Public Health and the Environment, Bilthoven, The Netherlands

    Tjallie van der Kooi, Sabine de Greeff & Jaap van Dissel

  2. Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Raemistrasse 100, 8091, Zurich, Switzerland

    Hugo Sax, Lauren Clack & Walter Zingg

  3. Department of Infectious Diseases, Bern University Hospital and University of Bern, Bern, Switzerland

    Hugo Sax

  4. Medical Center – University of Freiburg, Freiburg, Germany

    Hajo Grundmann

  5. University of Geneva Hospitals, Geneva, Switzerland

    Didier Pittet

  6. WHO Collaborating Centre on Infection Prevention and Control and Antimicrobial Resistance, Geneva, Switzerland

    Didier Pittet & Walter Zingg

  7. Center for Health Services and Outcomes Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA

    Albert W. Wu

  8. Galway University Hospital, Galway, Ireland

    Judith Davitt

  9. Evangelismos Hospital, Athens, Attica, Greece

    Sofia Kostourou & Fani Veini

  10. St. Michaels Hospital, Dún Laoghaire, Ireland

    Alison Maguinness

  11. Faculty of Health Sciences, University of Bielsko-Biala, Bielsko-Biala, Poland

    Anna Michalik

  12. Emergency Institute for Cardiovascular Diseases “Prof. C.C. Iliescu”, Bucharest, Romania

    Viorica Nedelcu

  13. Bács-Kiskun Megyei Kórház (County Teaching Hospital), Kecskemet, Hungary

    Márta Patyi & Éva Varga

  14. University Medical Centre Ljubljana, Ljubljana, Slovenia

    Janja Perme Hajdinjak & Milena Prosen

  15. Hospital Vall d’Hebron, Barcelona, Catalunya, Spain

    David Tellez

  16. John Paul II Hospital, Kraków, Poland

    Mirosław Ziętkiewicz

  17. Medical College Jagiellonian University, Kraków, Poland

    Mirosław Ziętkiewicz

Authors
  1. Tjallie van der Kooi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hugo Sax
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hajo Grundmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Didier Pittet
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sabine de Greeff
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jaap van Dissel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lauren Clack
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Albert W. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Judith Davitt
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sofia Kostourou
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alison Maguinness
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Anna Michalik
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Viorica Nedelcu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Márta Patyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Janja Perme Hajdinjak
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Milena Prosen
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. David Tellez
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Éva Varga
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Fani Veini
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Mirosław Ziętkiewicz
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Walter Zingg
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

the PROHIBIT consortium

Contributions

TK, WZ, HS, HG and DP were involved in study design and analysis. JDA, SK, AMA, AMI, VN, MPA, JP, MPR, DT, EV, FV and MZ collected the data and were involved in implementing the intervention. TK and WZ drafted the initial manuscript, to which HS and HG made major contributions and which was revised by all co-authors, including SG, LC, AW and JD. TK, WZ, HS and HG are guarantors. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Walter Zingg.

Ethics declarations

Ethics approval and consent to participate

The Medical Ethical Research Committee of the Utrecht University Medical Center, Utrecht, the Netherlands, decided that the study was not subject to the Medical Research Involving Human Subjects Act. All institutional review boards of the study hospitals approved of the study protocol.

Consent for publication

Not applicable.

Competing interests

HS served at the Swiss Federal Office of Public Health, Swissnoso (Nation center for Infection Prevention) and the World Health Organisation, received fee/honoraria for delivering training/lectures at Agfam, Switzerland; AstraZeneca; Walker Projects, Switzerland and the Digital Health Conference, Switzerland; participates in the Advisory board of Aseptuva and is CEO and owner of saxhealthdesign.com. JD is member of the data safety monitoring board of the SAFE trial (clinical study on treatment duration of S. aureus bacteraemias), unpaid. All the other authors have no competing interests to declare.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1: Table S1.

Association between hand hygiene and activity index during baseline and intervention; Table S2. Overall (i.e. for all HCWs) hand hygiene compliance per hospital, during baseline and the intervention period; Figure S1. Boxplot (median, quartiles and ranges) of individual HH compliance both during baseline and intervention period; Figure S2. Boxplot (median, quartiles and ranges) of hand hygiene increase (in percentage points) in the group of improving HCWs; Figure S3. Variability within improving HCWs, measured as the range between the observation session with the lowest compliance and the session with the highest compliance for each HCW.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

van der Kooi, T., Sax, H., Grundmann, H. et al. Hand hygiene improvement of individual healthcare workers: results of the multicentre PROHIBIT study. Antimicrob Resist Infect Control 11, 123 (2022). https://doi.org/10.1186/s13756-022-01148-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13756-022-01148-1

Keywords

Antimicrobial Resistance & Infection Control

ISSN: 2047-2994

Contact us