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Using and Sharing Findings from Surveillance: Rates, Ratios Proportions, Data Display & OUTBREAKS Russ Olmsted, MPH, CIC

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Presentation on theme: "Using and Sharing Findings from Surveillance: Rates, Ratios Proportions, Data Display & OUTBREAKS Russ Olmsted, MPH, CIC"— Presentation transcript:

1 Using and Sharing Findings from Surveillance: Rates, Ratios Proportions, Data Display & OUTBREAKS Russ Olmsted, MPH, CIC olmstedr@trinity-health.org

2 Reporting HAI Data; Definitions Larger size image on document at end of this section see also: Lee TB, et al Recommended practices

3 Incidence rate (“incidence density”) Number of new cases ––––––––––––––––––––––––––––––– Avg. population at risk × Time interval Number of new cases = –––––––––––––––––––– Population-time

4 Prevalence; another important measure Number of existing (new + old) cases Prevalence = –––––––––––––––––––––––––––– Population at risk Often expressed as a proportion

5 Population at risk Existing cases: Prevalence Outcomes Incident cases: number of kernels popped as heat is applied Kernals that go unpopped = not susceptible

6 Incidence and Prevalence Incidence and prevalence measure different aspects of disease occurrence Prevalence Incidence Numerator: Denominator Measures: Most useful: All cases, no matter how long diseased Only NEW cases All persons in population Only persons at risk of disease Presence of disease Risk of disease Resource allocation Risk, etiology

7 Device Utilization Ratio Device utilization ratio (DUR) is the proportion of patient or resident days for which a certain device is used DUR is specific to one device;e.g. central line, urinary catheter DUR reflects the amount of devices used and can be a reflection of patient severity or over utilization

8 Standardized Infection Ratio (SIR) –Observed # of HAIs SIR = ------------------------------------------------- –Expected (Predicted) # of HAIs Observed # of HAI – the number of events identified from surveillance HAIs  Expected or predicted # of HAI – comes from national baseline data, e.g. NHSN

9 Who’s Using SIR? CMS http://www.medicare.gov/hospitalcompare/search.html

10 PERFORMANCE MEASURES: Process Examples of process measures: 1) Compliance with educational program: calculate percent of personnel who have proper training on urinary cath. insertion # persons who insert catheters and are trained __________________________________ # personnel who insert urinary catheters Multiply by 100 to express as percent

11 Examples of other Uses of Process Measures Hand Hygiene (HH) Adherence: Numerator: # of Instances HH was used ___________________________________ X 100 Denominator: # of opportunities for HH ++++++++++++++++++++++++++++++++++++++++++++++++++++

12 PERFORMANCE MEASURES 3. Compliance with documentation of indication for catheter placement # number of patients on unit with catheter & proper documentation of indication ____________________________________ # of patients on unit with catheter Multiply by 100 to express as percent

13 Outcome Measures Incidence Density  Device-associated (DA) rates are calculated as Incidence Density Rates (IDRs)  What is an “Incidence Density Rate”?  Numerator = # of new cases during a period of time  Denominator = person-time or device day during -same time X a constant, e.g. 100, 1000, or 10,000

14 Outcome Measures, cont. Recommended outcome measures: 1.Rates of CAUTI – based on surveillance definitions # of cases of CAUTI ______________________________ total # of urinary-catheter days Multiply by 1000 to express as # infections per 1000 catheter days

15 Sharing & Displaying Data PA Patient Safety Advisory, 2010

16 Trend Analysis; CDI rates by reporting Quarter Cumulative CDI rates [community-onset + hospital onset] MDCH. SHARP Michigan 2012 Quarter 4 HAI Surveillance Report

17 Trend Analysis; CDI rates by reporting Quarter Cumulative CDI rates [community-onset + hospital onset] MDCH. SHARP Michigan 2012 Quarter 4 HAI Surveillance Report

18 Distribution of LabID CDI by type Cumulative CDI rates [community-onset + hospital onset] MDCH. SHARP Michigan 2012 Quarter 4 HAI Surveillance Report

19 Display of SIR TN Report on HAIs, June 2012 Larger size image on document at end of this section

20 Description of Outbreaks, U.S. Hospitals 35% of 855 hospitals who completed a survey had investigated possible outbreak. Frequency = 1.3 investigations/facility/24 months Triggers: –unusual organism (38.1%); –rate above baseline for a specific HAI site or for a specific unit were also frequent triggers (27%) Pathogens: –Norovirus (18.2%) –Staphylococcus aureus (17.5) –Acinetobacter spp (13.7) –Clostridium difficile (10.3) Rhinehart E, et al. AJIC 2012

21 Date of download: 10/19/2012 Copyright © 2012 American Medical Association. All rights reserved. From: Hospitalizations and Mortality Associated With Norovirus Outbreaks in Nursing Homes, 2009-2010 JAMA. 2012;():1-8. doi:10.1001/jama.2012.14023 a For norovirus outbreaks, outbreaks were counted as occurring during the week the first ill case was noted to be symptomatic. 308 Nursing Homes reported Oubreaks, Jan.2009-Dec. 2010 >67,000 hospitalizations; 26,000 deaths

22 Operational Definition of Outbreak/Cluster Occurrence of more cases of disease than expected in a given area among a specific group of people over a particular period of time Example: cluster of acute gastroenteritis cases is detected in the healthcare facility

23 Prevention and Control Measures: Influenza in LTCFs Control of Influenza Outbreaks in Long-Term Care Facilities Definition of Cluster: Three or more cases of acute febrile respiratory illness (AFRI) occurring within 48 to 72 hours, in residents who are in close proximity to each other (e.g., in the same area of the facility). Outbreak: A sudden increase of AFRI cases over the normal background rate or when any resident tests positive for influenza. One case of confirmed influenza by any testing method in a long-term care facility resident is an outbreak. –http://www.cdc.gov/flu/professionals/infectioncontrol/longtermcare.htm

24 Steps of an outbreak investigation Confirm outbreak and diagnosis ! Define case Identify cases and obtain information Descriptive data collection and analysis Develop hypothesis Analytical studies to test hypotheses Special studies Communication, including outbreak report Implement control measures

25 Role of the Infection Preventionist Identification of risk factors Identification of emergency control/long term interventions Documentation Best practice identification

26 DAY CASES Confirmation Outbreak Detection and Response Detection/ Reporting First Case(s) First Response Long term controls Evaluation

27 Describe the cases Descriptive epidemiology -Who is sick? -Where are they (unit, city, etc.) -When did they become ill? -What were they exposed to?

28 Person Place Time Cases Evaluate information Pathogen? Source? Transmission?

29 Note: Line listing may also be used for routine surveillance of HAIs.


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