public health and evaluation applications module 10

Public Health and Evaluation Applications

Module 10

Overview

• Surveillance of Public Health Outcomes.

• Focusing an Evaluation of System Change

• Evaluation Tools for Systems Change

Quality Indicators and SPCPublic Health Applications

• Improving Health in the Community: A Role for Performance Monitoring– IOM 1997

• Lead states in public health quality improvement: A Multistate Learning Collaborative. Robert Wood Johnson Foundation.

• Extensive use of SPC in Patient Safety.

Quality Indicators and SPCPublic Health Applications

HospitalEpidemiology is the classicalplace for SPCin epidemiology.

Other Areas with Wide Epidemiological Application

• Infectious Disease (outbreak detection)

• Syndromic Surveillance

• Detection of Bioterrorism

Environmental health and injury prevention can make powerful use of this material.

How to focus an evaluation

• Describe purpose

• Identify users of evaluation results

• Identify questions the evaluation should answer.

• Describe methods to answer these questions.

• Create agreements on who will do what to evaluate the initiative.

Think-Pair-ShareEvaluating Improvement Initiatives

• You have been asked to lead an evaluation team for a national improvement collaborative.

• 10 clinics have agreed to empanel their patients to care teams, experiment with small tests of change, and meet together every month to share lessons learned.

Think-Pair-ShareEvaluating Improvement Initiatives

• List 3 questions you would want the evaluation to answer.

• How would you answer these three questions?

Evaluation Questions for Multi-site Systems Change Interventions

• Naïve—Does it work?

• Less Naïve—Can it work?

• Ideal—What works for whom under what circumstances?

Consequences

• Non-linear response

• Cannot isolate the effects of the intervention– Depends too much on the changing context in

which the intervention occurred

The classic evaluation design

Group Pre-Test Intervention Post-Test

Intervention

O1 X O2

Control

O1 O2

How is the control group selected in the classic design?

Does this design answer the question:

What works for whom under what circumstances?

The realist evaluation designContext Pre-Test Intervention Post-Test

Large sites with inpatients O1 X O2

Small sites with inpatients O1 X O2

Large sites, ambulatory only

O1 X O2

Small sites,

Ambulatory only.

O1 X O2

Richer evaluation design

Context Pre-Test** Degree of

Implement-ation***

Post-Test**

Site 1

(Describe)*

O1 Full O2

Site 2

(Describe)*

O1 Partial O2

Site3

(Describe)*

O1 Minimal O2

Site 4

(Describe)*

O1 Full O2

*Qualitative **Quantitative ***Mixed

Even richer evaluation design

Context Baseline Timeseries**

Degree of

Implement-ation***

Post-Intervention Timeseries**

Site 1

(Describe)*

O1-O60 Full O61-O84

Site 2

(Describe)*

O1-O60 Partial O61-O84

Site3

(Describe)*

O1-O60 Minimal O61-O84

Site 4

(Describe)*

O1-O60 Full O61-O84

*Qualitative **Quantitative ***Mixed

Tools for richer and even richer designs

• Meta-analysis of before-after or beginning-ending results.

• “Dose-Response”– Sensitivity analysis of forest chart.– Regression of outcome on degree of

implementation.

• Mixed-Method Evaluation• SPC set up for evaluation.• Time Series Analysis

Meta-analysis of studies

Forest Chart of IPC Sites

Assessing “Dose-Response” and circumstances that predict success with sensitivity analysis

• Plot the forest chart again.

• Retain only sites with greater than 50 percent of patients empanelled (or any other variable of interest).

• Compare results with original forest chart.

• Look at the effect restricting analysis had on the “All Sites” observation.

Assessing “dose-response” with regression

Y = α + βxWhere:

Y = Difference between ending and beginning rates.α = Interceptx = percent of patients empanelled.Β = regression coefficient for x

Assessing “dose-response” with regression

Y = α + β1x1 + β2x2 Where:

Y = Difference between ending and beginning rates.α = Interceptx1 = percent of patients empanelled.β1 = regression coefficient for x1

x2 = self management support (1=yes, 0=no).β2 = regression coefficient for x2

Mixed Method Evaluation

Quantitative(Statistics)

Qualitative(Stories)

Mixed Method Evaluation

• Quantitative– Assess change before/after.– Compare to control group.– Compare outcome across multiple intervention sites.– Test hypotheses generated by qualitative.

• Qualitative (in-depth interviews, groups)– Rich description of implementation– Rich description of context (barriers, facilitators).– Compare high performers with low performers on

qualitative results.– Generate hypotheses for differences in performance.

Using control limits from pre-intervention baseline.

ConcludingQuote from Dr. Don Berwick

“Many assessment techniques developed in engineering and used in quality improvement—statistical process control, time series analysis, simulations, and factorial experiments—havemore power to inform about mechanisms and contexts thando RCTs, as do ethnography, anthropology, and other qualitative methods. For these specific applications, these methods are not compromises in learning how to improve; theyare superior.”

Donald BerwickJAMA. 2008;299(10):1182-1184