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Improved methods for estimating incidence from linked hospital morbidity data.

Kate J Brameld1, C D'arcy J Holman, David M Lawrence

  • 1Department of Public Health, The University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, Australia. kate@dph.uwa.edu.au

International Journal of Epidemiology
|August 13, 2003
PubMed
Summary

The backcasting method corrects overestimated disease incidence from hospital data. This approach accurately estimates chronic disease trends using linked health records, offering a cost-effective alternative to surveys.

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Area of Science:

  • Epidemiology and Public Health
  • Health Services Research
  • Biostatistics

Background:

  • Linked hospital morbidity data are valuable for estimating chronic disease incidence.
  • First-time hospital admission data often overestimate incidence due to including prevalent cases.
  • A novel backcasting method has been developed to address this overestimation.

Purpose of the Study:

  • To introduce and validate the backcasting method for accurate disease incidence estimation.
  • To correct incidence rates derived from linked hospital morbidity data.
  • To demonstrate the method's utility with diabetes mellitus and acute myocardial infarction data.

Main Methods:

  • Implemented a retrograde survival model to quantify incidence over-ascertainment.

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  • Calculated correction factors based on the duration of linked data.
  • Validated the method using linked data for diabetes and acute myocardial infarction against the Perth MONICA database.

    • Main Results:

    • Produced corrected incidence estimates for diabetes mellitus and acute myocardial infarction.
    • Diabetes incidence was found to be lower than North American estimates, aligning with prevalence data.
    • Acute myocardial infarction incidence was overestimated by approximately 10% in initial calculations.

    Conclusions:

    • The backcasting method provides a new approach for estimating disease incidence trends from linked hospital data.
    • This method is easy to use with routinely collected data and is cost-effective compared to surveys or registers.
    • Potential applications include analyzing trends in healthcare procedures and pharmaceutical prescriptions.