Premature mortality due to social and material deprivation in Nova Scotia, Canada
Bibliographic record
Abstract
INTRODUCTION: Inequalities in health attributable to inequalities in society have long been recognized. Typically, those most privileged experience better health, regardless of universal access to health care. Associations between social and material deprivation and mortality from all causes of death--a measure of population health, have been described for some regions of Canada. This study further examines the link between deprivation and health, focusing on major causes of mortality for both rural and urban populations. In addition, it quantifies the burden of premature mortality attributable to social and material deprivation in a Canadian setting where health care is accessible to all. METHODS: The study included 35,266 premature deaths (1995-2005), grouped into five causes and aggregated over census dissemination areas. Two indices of deprivation (social and material) were derived from six socioeconomic census variables. Premature mortality was modeled as a function of these deprivation indices using Poisson regression. RESULTS: Premature mortality increased significantly with increasing levels of social and material deprivation. The impact of material deprivation on premature mortality was similar in urban and rural populations, whereas the impact of social deprivation was generally greater in rural populations. There were a doubling in premature mortality for those experiencing a combination of the most extreme levels of material and social deprivation. CONCLUSIONS: Socioeconomic deprivation is an important determinant of health equity and affects every segment of the population. Deprivation accounted for 40% of premature deaths. The 4.3% of the study population living in extreme levels of socioeconomic deprivation experienced a twofold increased risk of dying prematurely. Nationally, this inequitable risk could translate into a significant public health burden.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".