Leishmaniasis, conflict, and political terror: A spatio-temporal analysis
Bibliographic record
Abstract
BACKGROUND: Leishmaniasis has been estimated to cause the ninth largest burden amongst global infectious diseases. Occurrence of the disease has been anecdotally associated with periods of conflict, leading to its referral as a disease of 'guerrilla warfare.' Despite this, there have been few studies that quantitatively investigate the extent to which leishmaniasis coincides with conflict or political terror. METHODOLOGY: This study employed a longitudinal approach to empirically test for an association between cutaneous and visceral leishmaniasis incidence with occurrence of conflict and political terror at the national level, annually for 15 years (1995-2010). Leishmaniasis incidence data were collected for 54 countries, and combined with UCDP/PRIO Armed Conflict and Amnesty International political terror datasets. Mixed effects negative binomial regression models clustered at the country-level were constructed to evaluate the incidence rate ratios against the predictors, while controlling for wealth. Additionally, to understand how and why conflict-terror may be associated with leishmaniasis incidence, we conducted a historical analysis. We identify and discuss posited causal mechanisms in the literature, and critically assessed pathways by which leishmaniasis might occur in places and times of conflict-terror. RESULTS: There was a significant dose-response relationship for disease incidence based on increasing levels of conflict and terror. Country-years experiencing very high levels of conflict-terror were associated with a 2.38 times higher [95% CI: 1.40-4.05] and 6.02 times higher [95% CI: 2.39-15.15] incidence of cutaneous and visceral leishmaniasis, respectively. Historical analysis indicated that conflict and terror contribute to-or coincide with-leishmaniasis incidence through processes of population displacement and health system deterioration. CONCLUSIONS: This research highlights the potentially increased risks for cutaneous and visceral leishmaniasis incidence in areas of high conflict-terror. Notably, conflict-terror may act as an empirical proxy for indirect and concomitant processes of social breakdown, migration, and instability, which emerge as the dominant processes associated with disease incidence.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".