Improving investment in chronic disease care in Sub-Saharan Africa is crucial for the achievement of SDG 3.4: application of the chronic care model
Bibliographic record
Abstract
Over 41 million people die of chronic non-communicable diseases (CNCDs) each year, accounting for 71% of all global deaths. The burden of CNCD is specifically a problem in sub-Saharan Africa (SSA) since CNCDs are largely a leading major cause of mortality in the sub-region. While the disease burden and mortality from chronic non-communicable diseases (CNCDs) have reached an epidemic threshold in sub-Saharan Africa (SSA), health systems, policy-makers and individuals still consider CNCDs to be uncommon and, therefore, do not give its management the required attention. In sub-Saharan Africa (SSA), effectively addressing the growing burden of CNCDs will require comprehensive measures that incorporate both curative and preventive interventions, towards achieving the Sustainable Development Goal (SDG) 3.4 target of reducing by one-third premature mortality from CNCDs through prevention and treatment and the promotion of mental health and well-being by the year 2030. In this commentary, we adopt the Chronic Care Model (CCM) to discuss how improved investment in Chronic Disease Care is crucial in achieving the SDG target in SSA. At the health systems level of the CCM, we propose that countries in SSA should increase the proportion of their annual budgets allocated to health in line with the Abuja Declaration of 2001. Social health insurance should also be adopted by all countries and effectively implemented. At the community level, we propose intensified community-based health education, the formation of peer support groups and the implementation of community-based policies that promote healthy eating and physical activity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".