Chronic Disease
Bibliographic record
Abstract
Eighty percent of global deaths from heart disease, stroke, cancer, and other chronic diseases occur in low- and middle-income countries. This chapter discusses priorities for control of these chronic diseases as an input into the 2012 Copenhagen Consensus. This chapter and the accompanying Chapter 7 on infectious disease control build on the results of the 2008 Copenhagen Consensus chapter on disease control (Jamison et al ., 2008), and is best read as an extension of the latter chapter. This chapter also draws on the framework and findings of the Disease Control Priorities Project (DCP2). The DCP2 engaged over 350 authors and among its outputs were estimates of the cost-effectiveness of 315 interventions, including about 100 interventions for chronic diseases. These estimates vary a good deal in their thoroughness and in the extent to which they provide regionally-specific estimates of both cost and effectiveness. Taken as a whole, however, they represent a comprehensive canvas of chronic disease control opportunities. This chapter identifies five key priority interventions for chronic disease in developing countries which chiefly address heart attacks, strokes, cancer, and tobacco-related respiratory disease. These interventions are chosen from among many because of their cost-effectiveness, the size of the disease burden they address, their implementation ease, and other criteria. Separate but related 2008 Copenhagen Consensus chapters dealt with other major determinants of chronic diseases such as nutrition, (Behrman et al ., 2007), air pollution (Larsen et al ., 2008) and education (Orazem et al ., 2008). The health-related chapters for the 2012 Copenhagen Consensus focus on infectious diseases (Jamison et al ., 2012), sanitation and water (Rijsberman and Zwane, 2012), education (Orazem, 2012), hunger and undernutrition (Hoddinott et al ., 2012) and population growth (Kohler, 2012).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.106 | 0.027 |
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".