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Increase control of cholesterol.

Causal Attribution: Is progress on outcomes due to your program or intervention? In public
health practice, causal attribution is often difficult to ascertain, especially for your more
distant outcomes. However, determining causality between your activities/outputs and your
short-term outcomes can often be accomplished without too much effort. Usually, surveys
and interviews, or analysis of records can establish causality at that level. And the brief time
duration for short-term outcomes usually insures that causal results can be determined in a
relatively small amount of time. By using theories of change to develop your logic model
you can assume, with more confidence, that intermediate and long-term outcomes are a
result of your short-term outcomes. Therefore, it is important to establish causality between
at least the activities (and resulting outputs) you carry out and the short-term outcomes.
Figure 2: Evaluation Domains
Evaluation Domains
Activities Inputs Outputs
(link between boxes)
Causal Attribution
(progression between boxes)
The boxes and arrows in Figure 2 indicate evaluation points or places where it is logical to ask
evaluation questions. As the program or intervention progresses through the logic model—as the
intervention matures—new series of evaluation questions can be identified. Outcome evaluation
looks back over the entire model. If based on a good process evaluation, the logic model can help
identify reasons for less than successful interventions by asking “where did the model break down?”
Using this thinking, the logic model can facilitate mapping evaluation questions and indicators as
shown in Figure 3.
Logic Models Page 8
Figure 3: Mapping Evaluation Questions and Indicators to the Logic Model
Mapping Evaluation Questions and
Indicators to a Logic Model
Outcome Process
Outcomes Long-term
HDSP Program Logic Model
The Healthy People 2010 Objectives for Heart Disease and Stroke are national goals to unify and
focus work done by states, federal agencies, and non-profit agencies. State HDSP programs are
not directly responsible for these long-term, high-level outcomes; however, state interventions and
accomplishments contribute to achieving them. Typically, surveillance data are used to track
progress on such long-term outcomes.
The CDC HDSP program logic model is provided in Appendix 1. The logic model was developed to
describe the processes and events that are expected from combined state and federal resources
and activities to prevent heart disease and stroke. CDC and State activities are outlined in terms of
capacity building, surveillance, and interventions. These activities and outcomes result in changes
in policy and environmental supports (intermediate outcomes), which in turn influence system or
population changes and improve health status (long-term outcomes). A population decrease in
premature death and disability (impact) is the ultimate result of program activities. As programs
focus efforts on disparate populations, these activities are also expected to eliminate disparities
between general and priority populations.