Burden of disease, health indicators and challenges for epidemiology in North America
Bibliographic record
Abstract
BACKGROUND: Commissioned by the International Epidemiological Association, this article is part of a series on burden of disease, health indicators and the challenges faced by epidemiologists in bringing their discoveries to provide equitable benefit to the populations in their regions and globally. This report covers the health status and epidemiological capacity in the North American region (USA and Canada). METHODS: We assessed data from country-specific sources to identify health priorities and areas of greatest need for modifiable risk factors. We examined inequalities in health as a function of social deprivation. We also reviewed information on epidemiological capacity building and scientific contributions by epidemiologists in the region. FINDINGS: The USA and Canada enjoy technologically advanced healthcare systems that, in principle, prioritize preventive services. Both countries experience a life expectancy at birth that is higher than the global mean. Health indicator measures are consistently worse in the USA than in Canada for many outcomes, although typically by only marginal amounts. Socio-economic and racial/ethnic disparities in indicators exist for many diseases and risk factors in the USA. To a lesser extent, these social inequalities also exist in Canada, particularly among the Aboriginal populations. Epidemiology is a well-established discipline in the region, with many degree-granting schools, societies and job opportunities in the public and private sectors. North American epidemiologists have made important contributions in disease control and prevention and provide nearly a third of the global scientific output via published papers. CONCLUSIONS: Critical challenges for North American epidemiologists include social determinants of disease distribution and the underlying inequalities in access to and benefit from preventive services and healthcare, particularly in the USA. The gains in life expectancy also underscore the need for research on health promotion and prevention of disease and disability in older adults. The diversity in epidemiological subspecialties poses new challenges in training and accreditation and has occurred in parallel with a decrease in research funding.
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 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 | 0.033 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".