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Assessing the quality of cause of death data in six high-income countries: Australia, Canada, Denmark, Germany, Japan and Switzerland

  • Lene Mikkelsen
  • Kim Moesgaard Iburg
  • Tim Adair
  • Thomas Fürst
  • Michael Hegnauer
  • Elena von der Lippe
  • Lauren Moran
  • Shuhei Nomura
  • Haruka Sakamoto
  • Kenji Shibuya
  • Annelene Wengler
  • Stephanie Willbond
  • Patricia Wood
  • Alan D. LopezEmail author
Original article
  • 59 Downloads

Abstract

Objectives

To assess the policy utility of national cause of death (COD) data of six high-income countries with highly developed health information systems.

Methods

National COD data sets from Australia, Canada, Denmark, Germany, Japan and Switzerland for 2015 or 2016 were assessed by applying the ANACONDA software tool. Levels, patterns and distributions of unusable and insufficiently specified “garbage” codes were analysed.

Results

The average proportion of unusable COD was 18% across the six countries, ranging from 14% in Australia and Canada to 25% in Japan. Insufficiently specified codes accounted for a further 8% of deaths, on average, varying from 6% in Switzerland to 11% in Japan. The most commonly used garbage codes were Other ill-defined and unspecified deaths (R99), Heart failure (I50.9) and Senility (R54).

Conclusions

COD certification errors are common, even in countries with very advanced health information systems, greatly reducing the policy value of mortality data. All countries should routinely provide certification training for hospital interns and raise awareness among doctors of their public health responsibility to certify deaths correctly and usefully for public health policy.

Keywords

Causes of death Medical certification Data quality Garbage codes Assessment of data 

Notes

Funding

Funding was provided by Melbourne Research, University of Melbourne.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

No data were collected from individual participants from whom informed consent would be required.

Supplementary material

38_2019_1325_MOESM1_ESM.docx (18 kb)
Supplementary material 1 (DOCX 17 kb)
38_2019_1325_MOESM2_ESM.xlsx (12 kb)
Supplementary material 2 (XLSX 11 kb)

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Copyright information

© Swiss School of Public Health (SSPH+) 2020

Authors and Affiliations

  • Lene Mikkelsen
    • 1
  • Kim Moesgaard Iburg
    • 2
  • Tim Adair
    • 1
  • Thomas Fürst
    • 3
  • Michael Hegnauer
    • 3
  • Elena von der Lippe
    • 4
  • Lauren Moran
    • 5
  • Shuhei Nomura
    • 6
  • Haruka Sakamoto
    • 6
  • Kenji Shibuya
    • 7
  • Annelene Wengler
    • 4
  • Stephanie Willbond
    • 8
  • Patricia Wood
    • 8
  • Alan D. Lopez
    • 1
    Email author
  1. 1.Bloomberg Data for Health InitiativeUniversity of MelbourneMelbourneAustralia
  2. 2.Institute of Public HealthAarhus UniversityÅrhusDenmark
  3. 3.Swiss Tropical and Public Health InstituteBaselSwitzerland
  4. 4.Department of Epidemiology and Health MonitoringRobert Koch InstituteBerlinGermany
  5. 5.Australian Bureau of StatisticsCanberraAustralia
  6. 6.Department of Global Health Policy, Graduate School of MedicineThe University of TokyoTokyoJapan
  7. 7.University Institute of Population HealthKing’s College LondonLondonUK
  8. 8.Statistics CanadaOttawaCanada

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