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Descriptive methods for evaluation of state-based intervention programs.

William W Davis1, Barry I Graubard, Anne M Hartman

  • 1Institute for Global Tobacco Control, Johns Hopkins Bloomberg School of Public Health, USA.

Evaluation Review
|October 9, 2003
PubMed
Summary
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Evaluating intervention studies requires accounting for routine, time-interval data. Ignoring between-state variations in tobacco consumption can lead to overly optimistic confidence limits for intervention effects.

Area of Science:

  • Public Health
  • Biostatistics
  • Intervention Science

Background:

  • Program evaluation often involves analyzing routinely collected data at fixed time intervals.
  • State-level differences in outcomes, such as tobacco consumption, are common and require statistical consideration.
  • The American Stop Smoking Intervention Study provides a relevant case study for evaluating public health interventions.

Purpose of the Study:

  • To discuss program evaluation methods for intervention studies with routinely collected outcome data.
  • To highlight the importance of accounting for between-state variations in analyses.
  • To demonstrate how ignoring these variations can affect the interpretation of intervention effects.

Main Methods:

  • Utilizing data from the American Stop Smoking Intervention Study.

Related Experiment Videos

  • Analyzing state per capita tobacco consumption as the primary outcome.
  • Comparing analytical approaches with and without consideration of between-state effects.

    • Main Results:

    • States exhibit significant differences in mean tobacco consumption, necessitating their inclusion in analyses.
    • Failure to account for between-state variation in intervention effects can substantially alter variance estimates.
    • Ignoring between-state effects often results in confidence limits that are unrealistically narrow (too optimistic).

    Conclusions:

    • Accurate program evaluation of interventions requires methods that address heterogeneity between study units (e.g., states).
    • Statistical models must incorporate between-state variability to provide valid confidence intervals for intervention effects.
    • Ignoring between-state differences can lead to misinterpretations of intervention effectiveness in public health studies.