5 notable papers for IPAC practice in Chingiz Amirov MPH, MSc-QIPS, CIC, FAPIC Director IPAC, Baycrest Health Sciences Editor-in-Chief, CJIC

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1 5 notable papers for IPAC practice in 2017 Chingiz Amirov MPH, MSc-QIPS, CIC, FAPIC Director IPAC, Baycrest Health Sciences Editor-in-Chief, CJIC

2 Disclosure Chingiz Amirov, Director of IPAC at Baycrest Health Sciences (Toronto) and Editor-in-Chief for the Canadian Journal of Infection Control (CJIC). His salary is supported by Baycrest Health Sciences. He has received no other funding for the research and information he will be presenting.

3 How I chose the papers Direct implications on IPAC practice Strong knowledge translation element Findings (intended or not) that get your critical juices flowing My personal & professional bias

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5 Observation changes behavior

6 Hawthorne effect

7 Study design 8 hrs/day x 3-5 days, 64 nurses, 5 ICUs, 4 hospitals Rates recorded using room entry & exit If HH in the first 2 hrs >20% than in the last 6 hrs, then the first 2 hrs were dropped and 2 additional hrs added at the end of observation period

8 HH compliance hr 2hr 3hr 4hr 5hr 6hr 7hr 8hr 9hr 10hr Time

9 HH compliance hr 2hr 3hr 4hr 5hr 6hr 7hr 8hr 9hr 10hr + Time

10 Study questions Can we measure the Hawthorne Effect? Does protracted overt observation significantly affect Hawthorne effect? Can an artificially high elective HH adherence be sustained beyond a couple of hours?

11 Conclusion Using the definition established for this study, it must be concluded that there was no Hawthorne effect present during this direct observational study and that the presence of an observer did not significantly alter the behavior of the nurses regarding their HH behavior

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13 Alternative conclusion Hawthorne effect is: Present Resilient Hard to measure through overt observation only

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15 The dreaded traffic light scorecards

16 Alternative conclusion OK for binary outcomes (e.g. complete/incomplete) and categorical variables Not OK for continuous variables (infection rate, HH compliance)

17 Seesawing run charts

18 Special vs. common cause

19 Open source data collection system Automated process control chart generation Commonly used IPAC metrics Charts adaptable to facility-specific needs

20 Free software

21 Special or common cause?

22 Special or common cause?

23 Instructions Data entry Chart creation Interpretation

24 In conclusion Quality improvement is central to IPAC programs Kiss traffic light scorecards goodbye Use SPC free SPC software Look like a pro (that you are)!

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26 Dispensers installed Wall-mounted HH dispensers installed Accred itation HH as PSI Panflu SARS Time

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29 Why change? Better dispensing system (e.g. automated) Compatible with electronic compliance monitoring Better price of ABHR & soap

30 Substantial savings Yes, ABHR is a commodity BUT The price difference percentage is in double-digits!

31 Harness the energy FTEs Operational $$$ BUDGET PRESSURE

32 A peer-to-peer guide Step-by-step instructions An Open Access article

33 A visual guide (brochure) Packed with lessons learned content/uploads/hce-prog- HH_HighQuality.pdf

34 Full Project Cycle Old product disposal

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36 Stakeholder map

37 In conclusion First step-by-step guidance on HH product transition Peer-to-peer: by ICPs for ICPs Packed with practical lessons learned Could be very useful or utterly useless

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39 Clostridium difficile infection in LTC Not tracked in AB or any other province We know colonization rate is high (~20%!) We know risk factors are prevalent But what s the incidence of clinical infection?

40 Case identification ACCIS CDRS DAD

41 Case identification ACCIS CDI CDRS DAD

42 Settings 172 LTC facilities 18 months 5,051,241 LTCF resident days

43 Key findings 6,945 CDI cases detected in the province Of these, 5.6% were LTCF-registered residents Of these 70.3% had LTCF onset of CDI 0.7 cases per 100,000 resident-days for acute care transfer LTC onset; and 1.4 cases per 100,000 resident-days for LTCF onset

44 In conclusion First such province-wide study in Canada First glimpse into the CDI baseline in LTCFs Caseload burden primarily on LTCF onset CDI

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46 Study summary Network-based modeling in simulated NICU Examines potential for per-hour infant-infant MRSA transmission via HCW Hand hygiene: how good is good enough to stop the spread of MRSA?

47 Modeling parameters Starting MRSA prevalence 2%-8% HH compliance 0% (none) - 100% (theoretical max) Expected effectiveness 88%

48 Key findings Mean # of MRSA transmissible opportunities: 1.3/hr Preterm infants: 61% increase in MRSA colonization Ventilated infants: 27% increase in MRSA colonization

49 HH Compliance MRSA reduction & HH compliance MRSA transmission reduction 0 Model 1 Model 2 Model 3 Model 4 Model 5 MRSA reduction (%) HH compliance (%)

50 HH Compliance HH & the law of diminishing returns HAI Incidence 20 0 Your Time/Energy 50

51 In conclusion HH compliance follows the law of diminishing returns How good is good enough for your facility? Focus on the most acute patients

52 In conclusion