Diarrhea illness in livestock keeping households in Cambodia: An analysis using a One Health framework
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".