P-167. New epidemiological routes of Coccidioidomycosis in Mexico – the extension of this pathogen to new areas
Notice bibliographique
Résumé
Abstract Background Due to specific growing conditions, Coccidioidomycosis is a fungal disease typically found in northern Mexico, near California or Arizona. However, due to climate change, there has been an increase in cases in non-endemic areas. In this study, we describe areas where cases of Coccidioidomycosis have been reported, which were previously not known to have this disease. Image of a map of Mexico that highlights both high-risk and low-risk areas. Dots represent origin and migration. Blue circles represent areas where patients lived while yellow represents areas where people migrated. The reason for migration was not studied. Pink circles represent those without travelling. Methods We developed a registry of Coccidioidomycosis cases, which collects data on sociodemographic, travel, and clinical conditions, including tomography, pathology, and outcomes. The patients were categorized into groups based on migration and geographical living area, and we collected data from physical or electronic health records. Results Between 1991 and 2023, we diagnosed 122 patients with coccidioidomycosis. The most common comorbidities were diabetes mellitus (41%) and overweight (24%). Forty-eight patients (39%) living in endemic areas had high-risk working conditions, such as construction, archaeology, and topography. Diagnosis was made using culture in 79.5% of cases, serology in 54%, and biopsy in 51%. CT scans showed predominant nodular (77%) and cavitary lesions (61%). Surprisingly, 46.7% of patients had no risk factors, such as travelling or living in endemic areas. 29.5% of patients had a history of migration as a risk factor to a high-risk area. The average time from symptom onset to diagnosis was 150 days (IC95 61-518 days). The patients were divided into four groups based on their risk factors. 8 (6.5%) lived and travelled in high-risk areas, 19 (15.5%) lived in high-risk areas without migration, 36 (29.5%) lived in low-risk, travelling to high-risk, and 57(46.7%) lived in low-risk and did not travel. Conclusion This division through risk factors highlights areas not known to be at risk, with patients without a history of travelling presenting with Coccidioidomycosis. We estimate an increasing number of fungal infections due to climate change, characterized by increased drought in some areas. As with other diseases, diagnosing Coccidioidomycosis outside of endemic areas should raise awareness of its expansion, and healthcare workers should consider it as a possible differential diagnosis. It is crucial to have this area known to consider resources for treatment. Disclosures Carlos Flores Nunez, PHD, Pfizer: Grant/Research Support
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».