Analysis of Mortality Trajectory Patterns in the Middle East and North Africa: Which Diseases Are the Deadliest?
Bibliographic record
Abstract
This study aims to cluster the MENA countries in terms of the shape of their trajectory patterns of mortality related to the leading causes, including communicable diseases, noncommunicable diseases, and injuries. This allows us to distinguish which diseases are the deadliest in what cluster of countries. A longitudinal cluster analysis is performed on the annual death counts, which are collected between 2007 and 2023 for the MENA countries. The method enables finding the optimal number of clusters of countries with similar trajectory patterns of mortality related to each category of diseases. Distinct trajectory patterns of death are identified, and diseases that are the leading causes of fatality in each country are described. Our results point to a concerning health burden expected to be related essentially to neurological conditions, neoplasms, and cardiovascular diseases in specific clusters of MENA countries. It is argued that rapid increases in death patterns sparked by these causes in the MENA countries require targeted health intervention that deals specifically with how to manage these priority diseases effectively to reverse their expected trend of higher fatalities over time. • Countries in the Middle East and North Africa present notable differences in the quality of healthcare they provide, and some diseases are the deadliest in specific clusters of countries. • A statistical analysis was performed to identify distinct clusters of countries with rapidly increasing mortality trajectory patterns due to certain diseases. • Health interventions that prioritize and target these high-risk diseases are needed to reduce their future burden.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".