Country of birth, socioeconomic position, and healthcare expenditure: a multilevel analysis of Malmö, Sweden
Bibliographic record
Abstract
STUDY OBJECTIVE: The principle of equity aims to guarantee allocation of healthcare resources on the basis of need. Therefore, people with a low income and persons living alone are expected to have higher healthcare expenditures. Besides these individual characteristics healthcare expenditure may be influenced by country of birth. This study therefore aimed to investigate the role of country of birth in explaining individual healthcare expenditure. DESIGN: Multilevel regression model based on individuals (first level) and their country of birth (second level). SETTING: The city of Malmö, Sweden. PARTICIPANTS: All the 52 419 men aged 40-80 years from 130 different countries of birth, who were living in Malmö, Sweden, during 1999. MAIN RESULTS: At the individual level, persons with a low income and persons living alone showed a higher healthcare expenditure, with regression coefficients (and 95% confidence intervals) being 0.358 (0.325 to 0.392) and 0.197 (0.165 to 0.230), respectively. Country of birth explained a considerable part (18% and 13%) of the individual differences in the probability of having a low income and living alone, respectively. However, this figure was only 3% for having some health expenditure, and barely 0.7% with regard to costs in the 74% of the population with some health expenditure. CONCLUSIONS: Malmö is a socioeconomically segregated city, in which the country of birth seems to play only a minor part in explaining individual differences in total healthcare expenditure. These differences seem instead to be determined by individual low income and living alone.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".