Integrating DHIS2 and R for Enhanced Cholera Surveillance in Lebanon: A Case Study on Improving Data Quality

Area of Science:

• Public Health
• Epidemiology
• Health Informatics

Background:

• Lebanon experienced a significant cholera outbreak in 2022-2023.
• Routine surveillance data collection via District Health Information System 2 (DHIS2) faced challenges with completeness and accuracy.

Purpose of the Study:

• To enhance the quality and utility of cholera surveillance data using automated R scripts.
• To improve data completeness, case classification, geographic accuracy, and laboratory result documentation.

Main Methods:

• Developed and implemented R scripts for automated data cleaning, standardization, and reclassification within DHIS2.
• Generated epidemic curves and incidence maps using R for spatial analysis.
• Shared R scripts on GitHub for open access and adaptation.

Main Results:

• Data completeness improved from high missingness (up to 99.7%) to full completeness for key variables.
• Case classification accuracy improved post-reclassification.
• Enabled better spatial analysis of cholera incidence and rapid diagnostic test coverage.
• Identified discrepancies in case reporting location versus residence.

Conclusions:

• Integrating R-based analytics with DHIS2 significantly enhanced cholera surveillance data quality.
• This approach provided a clearer epidemiological picture, supporting public health decision-making.
• Highlights the value of combining analytical tools with routine health surveillance systems.