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Related Experiment Videos

Evaluating disease management program effectiveness: an introduction to time-series analysis.

Ariel Linden1, John L Adams, Nancy Roberts

  • 1Linden Consulting Group, 6208 NE Chestnut Street, Portland, OR 97124, USA. ariellinden@yahoo.com

Disease Management : DM
|January 23, 2004
PubMed
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The total population approach for disease management (DM) program evaluation is flawed. Time-series analysis using utilization measures offers a more accurate method for assessing DM program effectiveness.

Area of Science:

  • Health Services Research
  • Biostatistics
  • Public Health

Background:

  • The prevalent
  • total population approach
  • for disease management (DM) program evaluation uses a pretest-posttest design.
  • This method lacks a control group, introducing potential bias and confounding factors.
  • Reliance on financial indicators over utilization metrics further complicates accurate program assessment.

Purpose of the Study:

  • To introduce time-series analysis as a superior alternative to the total population approach for DM program evaluation.
  • To highlight the limitations of current DM program evaluation methods.
  • To advocate for the use of disease-specific utilization measures in program assessment.

Main Methods:

Related Experiment Videos

  • Non-technical introduction to time-series analysis.
  • Application of time-series analysis using disease-specific utilization measures.
  • Comparison with the limitations of the total population approach.

Main Results:

  • Time-series analysis provides a more robust evaluation framework for DM programs.
  • Utilization measures are more appropriate than financial indicators for assessing program success.
  • Identifies and addresses limitations inherent in the total population approach.

Conclusions:

  • Time-series analysis offers a more scientifically sound method for evaluating disease management program effectiveness.
  • Shifting from financial to utilization metrics and employing time-series analysis can improve evaluation accuracy.
  • This approach mitigates biases associated with the total population method.