Socio-Demographic Determinants, Diagnostic Performance, and Prevalence Rates of Helicobacter pylori Infection Among Symptomatic Patients in Albieda City, Green Mountain Region, Libya

Résumé fourni par la source

Helicobacter pylori remains a paramount etiological agent underpinning chronic gastric pathologies globally, including gastritis, peptic ulcers, and gastric malignancies. Despite its significance, epidemiological profiles mapping this pathogen across North African communities remain critical for public health initiatives. This cross-sectional study elucidated the prevalence of infection, evaluated the associations of age and biological sex with H. pylori positivity, and assessed diagnostic performance among symptomatic outpatients in Albieda City, Green Mountain, Libya. A clinical sample of 103 outpatients (50 males and 53 females) with dyspeptic symptoms underwent testing using stool antigen capture assays or rapid urease tests. Statistical relationships were explored using independent-samples t-tests for age, Pearson's Chi-Square (χ2) test for gender, and Receiver Operating Characteristic (ROC) analysis. The baseline H. pylori point prevalence was 73.8% (76/103). Gender stratification revealed incidences of 76.0% among males (38/50) and 71.7% among females (38/53). The mean age of H. pylori-positive individuals (33.68 ± 11.59 years) showed no difference from H. pylori-negative counterparts (31.85 ± 12.87 years; p = 0.505). Cross-tabulations confirmed independence of biological sex and status (χ2 = 0.245, p = 0.620). However, the multi-factorial ROC analysis demonstrated diagnostic performance with an Area Under the Curve (AUC) of 0.792 (95% CI: 0.654–0.871, SE = 0.055, p < 0.001). The cohort manifests elevated H. pylori colonization, highlighting hyper-endemicity in the Green Mountain region. The uniform distribution across age and sex suggests generalized environmental exposure or early life transmission, negating demographic predispositions. The significant ROC performance indicates that clinical screening models can isolate colonization patterns to optimize resource allocation.