Association between individual-level and community-level socio-economic status and blood pressure among Inuit in Greenland
Bibliographic record
Abstract
BACKGROUND: Despite abundant evidence that socio-economic status (SES) is a fundamental determinant of health, there is a dearth of research examining association between SES, measured at the individual and community levels, and cardiovascular risk factors and morbidity among indigenous populations. OBJECTIVES: To examine the influence of individual-level and community-level SES on systolic and diastolic blood pressure among Greenlandic Inuit. METHODS: Multilevel analysis of cross-sectional data from the Inuit Health in Transition - Greenland Survey, to which 3,108 Greenlandic Inuit aged 18 years and older participated. Blood pressure is measured using an automatic device, according to standardized protocol. Individual SES is measured by education. Community socio-economic conditions are measured using combined information on average disposable household income and settlement type. RESULTS: Education was not significantly associated with blood pressure. There was an inverse U-shape association between community socio-economic conditions and blood pressure with significantly lower SBP and DBP among participants living in remote traditional villages characterized by lower average disposable household income and in affluent more urbanized towns. Sex-stratified analyses demonstrate the salience of community conditions for men. CONCLUSIONS: The association observed between blood pressure and community-level socio-economic conditions suggests that public health and social policies, programmes and interventions aiming to improve living conditions might improve cardiovascular health in Greenland. Studies are required to further examine social gradients in cardiovascular risk factors and morbidity among indigenous populations using different measures of SES.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".