Distribution and Risk Factors of Scrub Typhus in South Korea, From 2013 to 2019: Bayesian Spatiotemporal Analysis
Notice bibliographique
Résumé
Background: Scrub typhus (ST), also known as tsutsugamushi disease, is a common febrile vector-borne illness in South Korea, transmitted by trombiculid mites infected with Orientia tsutsugamushi, with rodents serving as the main hosts. Although vector-borne diseases like ST require both a One Health approach and a spatiotemporal perspective to fully understand their complex dynamics, previous studies have often lacked integrated analyses that simultaneously address disease dynamics, vectors, and environmental shifts. Objective: We aimed to explore spatiotemporal trends, high-risk areas, and risk factors of ST by simultaneously incorporating host and environmental information. Methods: ST cases were extracted from the 2013-2019 Korea National Health Insurance Service data at 250 municipal levels and by epidemiological weeks (International Classification of Diseases, Tenth Revision, Clinical Modification code: A75.3). Data on potential risk factors, including the maximum probability of rodent presence, area of dry field farming, forest coverage, woman farmer population, and financial independence, were obtained from publicly available sources. In particular, the maximum rodent presence probability was estimated using a maximum entropy model incorporating ecological and climate variables. Spatial autocorrelation was assessed using Global Moran I statistics with 999 Monte Carlo permutations. Spatial and temporal clusters were identified using Getis-Ord Gi* and hot and cold spot trend analyses. Bayesian hurdle models with a spatiotemporal interaction term, accounting for zero-inflated Poisson distribution, were used to identify associations between ST incidence and regional factors. Stratification analyses by gender and age group (0-39, 40-59, 60-79, and ≥80 years) were performed. Results: Between 2013 and 2019, 95,601 ST patients were reported. ST incidence had positive spatial autocorrelation (I=0.600; P=.01), with spatial expansion from southwestern to northeastern regions. Spatiotemporal models demonstrated better fit compared with spatial and temporal models, as indicated by lower Watanabe-Akaike information criterion (WAIC) values. Municipalities with higher rodent suitability (β coefficient=0.618; 95% credible interval [CrI] 0.425-0.812) and lower financial independence from central government (β coefficient=-0.304; 95% CrI -0.445 to -0.163) had higher likelihoods of increased ST incidence, even after adjusting for spatiotemporal autocorrelation. However, risk factors varied by age group: among individuals aged 40 years or older, ST incidence was positively associated with rodent suitability, while patients in the 0-39 years age group showed no association with rodent suitability (β coefficient=0.028; 95% CrI -0.072 to 0.126), and ST incidence was negatively associated with the women farmer population (β coefficient=-0.115; 95% Crl=-0.223 to -0.006). Conclusions: This is the first study to investigate ST in South Korea using a spatiotemporal framework grounded in a holistic One Health perspective. We elucidated the critical role of spatiotemporal dynamics in ST distribution, highlighting rodent suitability and economic independence as key drivers of disease distribution. Our findings lay the groundwork for evidence-based, region-specific intervention strategies and may inform targeted public health strategies in South Korea and other settings with similar ecological conditions.
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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».