Dengue IgM ELISA Dataset from Trinidad and Tobago: Analytical Evaluation of Borderline Serological Results and Seroprevalence Estimation

Background: Borderline dengue immunoglobulin M (IgM) enzyme-linked immunosorbent assay (ELISA) results create diagnostic and epidemiological uncertainty. Collapsing the manufacturer’s three qualitative categories into a binary outcome changes the effective decision rule and may materially alter the apparent positivity proportion. This Data article describes an anonymized laboratory dataset from Trinidad and Tobago and evaluates both category-handling uncertainty and test-misclassification uncertainty. Methods: The dataset comprises 161 consecutive serum specimens submitted for routine dengue IgM testing between 1 September 2025 and 28 February 2026 and processed in four analytical batches. The results were analyzed under three prespecified scenarios: borderline classified as negative, borderline excluded, and borderline classified as positive. Exact Clopper–Pearson 95% confidence intervals (CIs) were calculated. Rogan–Gladen adjustment was applied only to the borderline-excluded scenario because the ELISA test validation estimates of sensitivity and specificity were calculated after excluding borderline results. Results: Twenty specimens (12.4%) were positive, 29 (18.0%) borderline, and 112 (69.6%) negative. The apparent IgM positivity was 12.4% (20/161; 95% CI 7.8–18.5%) when borderline results were classified as negative, 15.2% (20/132; 95% CI 9.5–22.4%) when they were excluded, and 30.4% (49/161; 95% CI 23.4–38.2%) when they were classified as positive. For determinate results, the conditional Rogan–Gladen estimates were 13.2% using a sensitivity of 100.0% and specificity of 97.7%, and 15.1% using a sensitivity of 82.2% and specificity of 96.8%. Conclusions: Misclassification adjustment is informative but remains conditional on the transportability of external assay-performance estimates. The dataset supports transparent category-level reanalysis, while the absence of continuous index values, confirmatory testing, and detailed clinical metadata limits numerical cut-off recalibration and patient-level inference.