Prevalence of Zoonotic and Vector-Borne Infections Among Afghan National Army Recruits in Afghanistan
Bibliographic record
Abstract
OBJECTIVE: To measure prevalence of prior/current Plasmodium vivax and Plasmodium falciparum (PV and PF), Brucella spp. (BR), dengue virus (DENV), Leishmania donovani (visceral leishmaniasis; VL), and Crimean-Congo hemorrhagic fever (CCHF) virus exposure among Afghan National Army (ANA) recruits. METHODS: Randomly chosen, nationally representative serum samples from consenting men aged 18-40 years and who were screened between February 2010 and January 2011 were tested, with ∼25 samples/province. Samples were screened for PV and PF antigens and VL antibody with rapid diagnostic tests. Reactive malaria screening results were confirmed with polymerase chain reaction assay. Enzyme-linked immunosorbent assays were used to screen for CCHF and DENV antibodies; reactive DENV samples were confirmed with the plaque-reduction neutralization test. BR screening and confirmatory testing was performed with slide and tube agglutination, respectively. Correlates of BR titres >1:80 were analyzed using logistic regression. RESULTS: Of 809 participants contributing specimens, 62% had previously lived outside Afghanistan, predominantly in Pakistan and Iran. CCHF (4.1%, n = 33), DENV (2.1%, n = 17), and VL (1.0%, n = 8) antibody prevalence was low. For PV and PF, only 7 out of 56 reactive samples had detectable nucleic acid. For BR, 8.0% (n = 65) of samples had screening titers >1:40, of which 83.1% had confirmatory titers >1:80. Participants from Kabul and surrounding provinces had lower odds (OR = 0.19, 95% CI: 0.04-1.00) of BR antibody compared with other regions. CONCLUSIONS: BR exposure was relatively common with a nearly national distribution, whereas geographic distribution for other pathogens aligned roughly with the expected vector distribution. Public health protection measures should include vector control, food safety, and enhanced diagnostics for acute febrile illness.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".