Selective under-representation of Pacific peoples in population estimates for health indicator measurements in Aotearoa New Zealand misinforms policy making
Bibliographic record
Abstract
BACKGROUND: The Census of Populations and Dwellings' is the five yearly population count of Aotearoa New Zealand. Best available populations (BAP) are subnational projections based on census data and demographic assumptions developed for healthcare planning and funding allocation but are also used as the denominator for health indicator monitoring. Pacific people are systematically undercounted, but the impact on health statistics is not well studied. For COVID-19 vaccination coverage, health service user (HSU) data were considered a more reliable denominator than BAP but introduced new biases. We aimed to understand how the choice of denominator population impacts estimates of population size and health system performance for Pacific people at a local level. METHODS: We described how declining census response rates affected population data quality. We compared BAP and HSU data at district level. For the indicators 'access to primary care' and 'cervical cancer screening uptake' we replaced currently used BAP denominators with HSU and examined the impact for different ethnic groups in different geographic districts. RESULTS: Overall Census 2018 response declined by 10%, but for Māori and Pacific people by 21% and 23%, respectively. This inequitably affected BAP accuracy. Census undercount was highest in the district with the largest Pacific populations, where HSU exceeded BAP most. Notably, 'access to primary care' for Pacific people in this district consistently exceeds 100%. Using BAP, both health indicators are currently estimated as highest for Pacific people compared to other ethnic groups, but when based on HSU, they dropped to lowest. Similar, but less pronounced trends occurred in other districts. Changes in trends over time for both indicators coincided mostly with adjustments in BAP, rather than changes in the numerators. CONCLUSIONS: The current use of BAP denominators for health statistics does not enable reliable monitoring of key health indicators for Pacific people. HSU denominators are also unsuitable for monitoring health. Exploring the feasibility of a real-time population register is strongly recommended as a new, transparent, way of obtaining more reliable, timely population data to guide policymaking and underpin a more equitable health system under the health reforms. Meanwhile, reporting of ethnic specific outcomes need to include a clear assessment of the potential for bias due to inaccurate population estimates.
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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 | 0.069 | 0.192 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".