Transnational wealth-related health inequality measurement
Bibliographic record
Abstract
The study of international differences in wealth-related health inequalities has traditionally consisted of country-by-country comparisons using own-country relative measures of socioeconomic status, which effectively ignores absolute differences in both wealth and health that can differ between and within countries. To address these limitations, we propose an alternative approach: that of constructing a transnational measure of wealth-related health inequality. To illustrate the limitations of the country-by-country approach, we simulate the impact of changes in wealth and health inequalities both between and within countries on cross-country measures of health inequality and find at least five errors that may arise using country-by-country methods. We then empirically demonstrate the transnational approach to wealth-related health inequalities between and within Haiti and the Dominican Republic, the two constituent countries of the island of Hispaniola, using data from their respective Demographic and Health Surveys. Transnational socioeconomic rankings reveal a large and increasing divergence in wealth between the two countries, which would be ignored using the county-by-country approach. We find that wealth-related inequalities in long-term children's health outcomes are larger than inequalities in short-term health outcomes, and decompositions of the influence of place-based variables on these inequalities reveal country of residence to be the most important factor for long-term outcomes, while urban/rural residence and subnational regions are more important for short-term health outcomes. The significance of this novel methodological approach in relation to conventional health inequality research, including hidden dimensions of wealth-related health inequalities, for example the urbanized "middle class" distribution of HIV and a hidden unequal burden of wasting among children uncovered by the transnational approach are discussed, and errors in gauging changes in inequality over time using a country-by-country approach are highlighted. Using the transnational approach can help to measure important trends in wealth-related health inequalities across countries that more commonly used methods traditionally overlook.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".