Diarrhea illness in livestock keeping households in Cambodia: An analysis using a One Health framework
Notice bibliographique
Résumé
Background Most of human diarrheal pathogens are zoonotic, and transmission of the pathogens can occur by contaminated food, water, environment and direct contact with animals especially for livestock keepers. Yet little is known of the relative importance of different risk factors especially in under-studied countries. The objectives of this study were to identify risk factors for diarrhea in livestock keepers in Cambodia and detect diarrhea-causing pathogenic bacteria in both humans and livestock within a One Health approach. Of special interest were the links between diarrhea and food consumption and livestock-keeping. Materials and methods We used an existing dataset from a questionnaire survey conducted in 400 livestock farms in Prey Veng and Kampot Prefectures between February and March 2013 as well as laboratory results on bacterial isolation from fecal and swab samples from livestock and poultry, and human stool samples. Laboratory results were available for up to three animals of each species kept by a household, and for up to three human samples from households reporting at least one case of human diarrhea in the previous 2 weeks. Presence of Escherichia coli, Shigella spp. and Salmonella spp. was investigated in both animal and human samples, in addition to Aeromonas spp., Vibrio spp. and Plesiomonas spp. in animal samples and Campylobacter spp. in human samples. Univariable and multivariable risk factor analyses were performed by generalized linear mixed model. Results Household-level diarrhea incidence rate was 9.0% (36/400). The most statistically significant factor associated with diarrhea in multivariable analysis was water treatment for drinking and cooking (OR = 0.33, 95%CI: 0.16–0.69, p = 0.003), followed by number of days consuming egg within 2 weeks (OR = 1.16, 95%CI: 1.04–1.29, p = 0.008), number of children under 5 years old (OR = 1.99, 95%CI: 1.14–3.49, p = 0.016) and keeping poultry (OR = 0.36, 95%CI: 0.14–0.92, p = 0.033). Animal samples for bacterial culture test were collected at 279 cattle, 165 pig and 327 poultry farms, and bacteria were detected from 6 farms with the isolation of Escherichia coli O157 (non H7) from 1 cattle and 1 pig sample, Aeromonas caviae from 1 pig sample and Salmonella spp. from 3 chicken samples. In human samples, 17 out of 67 individual samples were positive for the culture test, detecting Escherichia coli O157 (non H7) from 7 samples and Shigella spp. from 10 samples. None of the households where target bacteria were detected from animal samples had human samples collected due to lack of diarrhea episodes in the household. Conclusions It has often been hypothesized that keeping livestock may increase the incidence of diarrhea through multiple pathways. Contrary to this, we found livestock-keeping was not associated with increased risk, but food-related behavior and children under 5 years of age were strongly associated with increased risk. We discuss mediating and confounding factors and make recommendations for reducing the burden of diarrheal disease in Cambodia and more widely in low- and middle-income countries.
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 | 0,008 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,005 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».