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A new data driven method for summarising multiple cause of death data.

Annette Dobson1, Paul McElwee2, Mohammad Reza Baneshi2

  • 1University of Queensland, School of Public Health, Brisbane, QLD, Australia. a.dobson@sph.uq.edu.au.

BMC Medical Research Methodology
|April 5, 2023
PubMed
Summary
This summary is machine-generated.

A new method for analyzing mortality statistics better reflects the impact of multiple conditions in older adults. This approach improves the accuracy of cause-of-death data for multimorbidity and aging populations.

Keywords:
Data-driven methodDeath ratesMultimorbidityMultiple causes of death

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Area of Science:

  • Gerontology
  • Public Health
  • Biostatistics

Background:

  • Current national mortality statistics rely on a single underlying cause of death.
  • This method inadequately represents the complexity of conditions in aging populations with high rates of multimorbidity.

Purpose of the Study:

  • To introduce a novel data-driven method for weighting causes of death.
  • To provide a more accurate representation of mortality, accounting for associations between underlying and contributing causes.

Main Methods:

  • Developed a new weighting methodology for mortality data based on cause-of-death associations.
  • Applied the method to Australian mortality data for individuals aged 60 years and over.

Main Results:

  • The new method reallocates higher death percentages to conditions like diabetes and dementia, often contributing causes.
  • It assigns lower percentages to closely related conditions such as ischemic heart disease and cerebrovascular disease.
  • Cancer mortality percentages remained similar to traditional methods when recorded as underlying causes.

Conclusions:

  • This data-driven approach offers a more nuanced understanding of mortality patterns in aging populations.
  • It can enhance national statistical agencies' reporting by complementing existing single-cause mortality tables.