Cautionary accounts in the use of health equity measures from linkable administrative data in population health intervention research
Bibliographic record
Abstract
ABSTRACT ObjectivesThere is increased interest in identifying strategies the reduce health inequities. With this focus, population health scientists have applied equity measures first developed in other disciplines to health equity research. The objective of this study is to illustrate the application of these measures in research using linkable administrative databases. This presentation will provide a brief description of some commonly-used equity measures and issues investigators face when applying them in their own health equity research. MethodsAnalyses focused on children born in Manitoba, 1984 to 2014. We used linkable administrative data from health, social services, and education to develop indicators of health and the social determinants of health. Income data from the Canadian Census were used to stratify children by socioeconomic status. Our study considered the distribution of several child outcomes: breastfeeding initiation, mortality, complete immunization rates at age 2, Grade 9 completion, and high school completion. We examined several measures often used to capture income-related health inequities: rate ratios and rate differences comparing children from high-income neighbourhoods with children from low-income neighbourhoods; the concentration index which quantifies the equity in the distribution of outcomes across the entire socioeconomic gradient; and the relative and absolute indices of inequality which compare the most advantaged individuals with the least advantaged individuals in the population while accounting for the distribution of health across the population. ResultsWhen these measures are applied to health equity, they can be affected by factors not initially considered by investigators. The application of Concentration measures using health outcomes that are frequently dichotomized, and the prevalence of the health outcome can affect the degree of inequity that is possible, with highly prevalent outcomes showing very little divergence from the line of equity. Comparing concentration measures to the inequality indices can produce contradictory and seemingly incompatible results. Sample selection that alters the distribution of income from the population can also change the apparent equity of health outcomes. These matters are complicated when monitoring changes in health equity, over time. ConclusionsSummary measures of equity can be useful but come with limitations that need to be considered when interpreting and applying study findings. We offer some suggestions to consider when applying these measures in health equity research.
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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 | 0.642 | 0.868 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.014 | 0.011 |
| Research integrity | 0.009 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".