Introduction: Prompt and accurate diagnosis of acute schistosomiasis is crucial for individual health and effective public health interventions. Schistosomiasis, often contracted in early childhood (within the first five years of life) in endemic regions, necessitates a focused understanding of its manifestations in this vulnerable age group. This research aimed to identify the prodromal signs and symptoms of early schistosomiasis infection, linking these to disease progression and risk assessment. The ultimate goal was to develop a user-friendly clinical algorithm for the Algorithmic Diagnosis Of Symptoms And Signs of early Schistosoma haematobium infection, specifically for resource-limited settings.
Methodology: A cohort study was conducted with 204 preschool-aged children, all lifelong residents of a schistosomiasis-endemic district and at high risk of infection. These children were monitored from July to December 2019, during a peak transmission season. Clinicians, blinded to the children’s schistosomiasis status, performed regular clinical evaluations and laboratory investigations. S. haematobium diagnosis was confirmed through urine filtration, with samples collected over three consecutive days. Initial signs and symptoms were compared across subsequent visits. Symptoms consistently observed during the last negative visit before a positive diagnosis were classified as early schistosomiasis infection (ESI) indicators, after excluding other potential causes. Logistic regression was used to identify key clinical predictors. A risk score model was developed based on these predictors to assess individual child risk, culminating in a diagnostic algorithm. This algorithm’s validity was then tested on a separate group of 537 preschool children.
Results: In the study, 21% (42) of participants initially tested negative for S. haematobium but later became positive. Compared to non-ESI participants, those in the ESI group exhibited specific prodromal signs and symptoms at their preceding negative visit. These included pruritic rash with an adjusted odds ratio (AOR) of 21.52 (95% CI 6.38-72.66), fever (AOR = 82, 95% CI 10.98-612), abdominal pain (AOR = 2.6, 95% CI 1.25-5.43), pallor (AOR = 4, 95% CI 1.44-11.12), and a history of facial or body swelling in the previous month (AOR = 7.31, 95% CI 3.49-15.33). Additionally, 16% of the ESI group showed mild normocytic anaemia, and 2% presented with moderate normocytic anaemia. The derived risk score model, based on rounded integer relative risk ratios, formed the basis of a diagnostic algorithm. This algorithm demonstrated a sensitivity of 81% and a specificity of 96.9%, with a Positive Predictive Value of 87.2% and a Negative Predictive Value of 95.2%. The algorithm’s area under the curve (AUC) was 0.93 (0.90-0.97), significantly outperforming urine dipstick tests with an AUC of 0.58 (0.48-0.69). Similar performance metrics were observed in the validation cohort.
Conclusion: This research is the first to identify specific prodromal signs and symptoms associated with early S. haematobium infection in preschool children. These early indicators facilitate timely intervention and management, mitigating the adverse effects of delayed diagnosis. The developed algorithmic diagnosis tool effectively risk-stratifies preschool children for early S. haematobium infection. Further independent validation on diverse cohorts is recommended to fully ascertain its utility and broad applicability as a tool for algorithmic diagnosis of symptoms and signs in schistosomiasis.
