Health facility-based prevalence of typhoid fever, typhus and malaria among individuals suspected of acute febrile illnesses in Southwest Region, Ethiopia

Prevalence, antimicrobial resistance pattern, and associated factors of Salmonella serovars among human-animal interfaces in the Amhara National Regional State, Ethiopia

BACKGROUND

Salmonellosis in humans and animals poses significant public health concerns, leading to substantial economic losses and increased healthcare costs in both developed and developing countries. This study aimed to assess the prevalence, antimicrobial resistance pattern, and associated factors of Salmonella serovars in humans and animals (sheep, chicken, and cattle) in the Amhara National Region state of Ethiopia.

METHODS

A prospective cross-sectional study was conducted using a consecutive convenience sampling technique from September 2022 to January 2024 at public health facilities and 25 randomly selected animal farms surrounding each health facility in the Amhara National Regional State. Socio-demographic data and relevant clinical characteristics were collected using a standardized questionnaire. After enrichment with Selenite F broth, Salmonella serovars were isolated using MacConkey, Xylose Lysine Deoxycholate, and Salmonella Shigella agar. The Salmonella serovars were characterized through a series of biochemical tests. The antimicrobial resistance patterns were determined using the modified Kirby-Bauer disk diffusion technique. Data analysis was performed using SPSS version 29, and the results were summarized in tables and graphs.

RESULT

The overall prevalence of Salmonella serovars among humans and animals was 12.1% (49/404; 95% CI: 9.2-15.6) and 2.7% (9/336; 95% CI: 1.3-4.8), respectively. Human isolates of Salmonella serovars exhibited high resistance rates to ampicillin (79.6%) and tetracycline (71.4%). Similarly, all animal isolates of Salmonella were found to be 100% resistant to both ampicillin and tetracycline. In contrast, a 2% resistance of Salmonella isolates was observed to ciprofloxacin among humans but 11% in animals. Data also showed that 71.4% and 88.9% of Salmonella isolates from humans and animals were resistant to three or more antibiotic classes, respectively. Close contact with patients experiencing diarrhea (AOR = 2.83; 95% CI: 1.05-7.629; P = 0.04) and with animals (AOR = 3.179; 95% CI: 1.574-6.419; P = 0.001) revealed a statistically significant association with the prevalence of culture-confirmed salmonellosis.

CONCLUSION

The prevalence of culture-confirmed Salmonella serovars among humans and animals was significantly high. Many isolates of Salmonella from these groups showed resistance to three or more classes of antibiotics. Individuals who come into contact with those experiencing diarrhea or who handle animals are at an increased risk of developing salmonellosis. To help reduce these infections, public health initiatives should focus on improving animal handling practices and promoting better hygiene in both the environment and personal habits through one health approach.

Enhancing Typhoid Fever Diagnosis Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Background: Typhoid fever remains a significant public health challenge, especially in developing countries where diagnostic resources are limited. Accurate and timely diagnosis is crucial for effective treatment and disease containment. Traditional diagnostic methods, while effective, can be time-consuming and resource-intensive. This study aims to develop a lightweight machine learning-based diagnostic tool for the early and efficient detection of typhoid fever using clinical data. Methods: A custom dataset comprising 14 clinical and demographic parameters-including age, gender, headache, muscle pain, nausea, diarrhea, cough, fever range (°F), hemoglobin (g/dL), platelet count, urine culture bacteria, calcium (mg/dL), and potassium (mg/dL)-was analyzed. A machine learning metamodel, integrating Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Decision Tree classifiers with a Light Gradient Boosting Machine (LGBM), was trained and evaluated using k-fold cross-validation. Performance was assessed using precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Results: The proposed metamodel demonstrated superior diagnostic performance, achieving a precision of 99%, recall of 100%, and an AUC of 1.00. It outperformed traditional diagnostic methods and other standalone machine learning algorithms, offering high accuracy and generalizability. Conclusions: The lightweight machine learning metamodel provides a cost-effective, non-invasive, and rapid diagnostic alternative for typhoid fever, particularly suited for resource-limited settings. Its reliance on accessible clinical parameters ensures practical applicability and scalability, potentially improving patient outcomes and aiding in disease control. Future work will focus on broader validation and integration into clinical workflows to further enhance its utility.