Assessing the quality of dengue data in the Philippines using Newcomb-Benford law

Abstract

Accurate and reliable data are vital for effective disease surveillance and control. This study examined the application of the Newcomb-Benford Law (NBL) as a tool for assessing the quality of dengue cases data in the Philippines. Large-scale datasets from the Epidemiology and Disease Control Surveillance (EDCS) and Philippine Integrated Disease Surveillance and Response (PIDSR) reports were analyzed to determine if the observed leading digit distributions deviate significantly from the expected NBL distribution. The statistical tests employed include the chi-squared test, Mantissa Arc Test, Mean Absolute Deviation (MAD), and distortion factor. The results reveal notable deviations from the expected NBL distribution, particularly in digits 1, 3, 4, 6, and 8, indicating potential irregularities and inconsistencies in the reported data. Factors contributing to these deviations may include data manipulation, measurement errors, and sampling biases. Improving data quality and integrity is crucial to ensure accurate disease surveillance.