Unlocking the numerator-denominator bias. II: Adjustments to mortality rates by ethnicity and deprivation during 1991-94. The New Zealand Census-Mortality Study.
Bibliographic record
Abstract
AIMS: Maori and Pacific mortality rates are underestimated due to different recording of ethnicity between mortality and census data--the so-called numerator-denominator bias. Ethnicity and deprivation are strongly associated with mortality in New Zealand, but it is unclear what are the independent and overlapping effects of each on health. The objectives of this study were first, to determine the effect of adjusting for numerator-denominator bias on ethnic-specific age-standardised all-cause mortality rates among 0-74 year olds during 1991-94: second, to determine the effect of adjusting for numerator-denominator bias on analyses of the independent associations of ethnic group and small area deprivation with all-cause mortality in New Zealand. METHODS: Direct standardisation methods were used to calculate rates of mortality by ethnic and small area deprivation groupings. RESULTS: Unadjusted for numerator-denominator bias, Maori had a 70% and 101% higher standardised mortality rate than non-Maori non-Pacific for males and females, respectively. Adjusting for numerator-denominator bias, the excess Maori mortality burden increased to 126% and 158%. For Pacific people, excess mortality increased from -5% and -13% (ie apparently lower mortality rates) to 58% and 54% after adjustment, for males and females respectively. Using data adjusted for numerator-denominator bias, about a third of the Maori to non-Maori non-Pacific disparity in mortality among 0-54 year olds was explained by small area deprivation. Conversely, about a quarter of the mortality gradient by deprivation in New Zealand was explained by ethnic group. CONCLUSIONS: Numerator-denominator bias causes a marked underestimate of the ethnic disparities in mortality in New Zealand for the 1991-4 period, both overall and within strata of deprivation. The distribution of small area deprivation by ethnicity explains some of the ethnic disparities in mortality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".