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Ethnic counts on mortality and census data (mostly) agree for 2001-2004: New Zealand Census-Mortality Study update
Tony Blakely1, June N Atkinson, Jackie Fawcett
1Section of Audiology, University of Auckland, Private Bag 92019, Auckland, New Zealand. pr.thorne@auckland.ac.nz.
The New Zealand Medical Journal
|September 18, 2008
Summary
New Zealand census and mortality data show minimal bias in ethnic counts when using a "total" ethnicity definition. This suggests mortality rate calculations are now accurate for public health research.
Area of Science:
- Public Health
- Biostatistics
- Epidemiology
Background:
- Previous studies (1980s-1990s) indicated significant undercounting of Maori and Pacific deaths in mortality data compared to census data.
- The New Zealand Census-Mortality Study (NZCMS) aimed to assess if this numerator-denominator bias persists with more recent data.
Purpose of the Study:
- To evaluate the accuracy of ethnic group counts in mortality data against census data.
- To determine if undercounting of specific ethnic groups in mortality records has been resolved.
Main Methods:
- Anonymously and probabilistically linked 2001 census data with 3 years of subsequent mortality records (82,404 records).
- Compared ethnicity recording between census and mortality datasets using both 'total' and 'sole' ethnicity definitions.
Main Results:
- Using a 'total' ethnicity definition, census and mortality counts showed close agreement for Maori (0.98 ratio), Pacific (0.98), Asian (1.02), and other groups (1.01).
- A 'sole' Maori ethnicity definition revealed census counts were only 86% of mortality counts, indicating under-ascertainment of multi-ethnic individuals in census data, particularly in the South Island.
Conclusions:
- Little bias exists between census and mortality data when using a 'total' ethnicity definition.
- Mortality rate calculations by ethnicity using unlinked data and a 'total' definition are likely unbiased.
- Results support the consistent use of the census definition of ethnicity across all health datasets.
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