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

Abass Toufic Jouny (), Hawraa Sweidan, Maryo Baakliny and Nada Ghosn
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Abass Toufic Jouny: Epidemiological Surveillance Program, Ministry of Public Health, Ras En Nabaa, Beirut 1107, Lebanon
Hawraa Sweidan: Epidemiological Surveillance Program, Ministry of Public Health, Ras En Nabaa, Beirut 1107, Lebanon
Maryo Baakliny: Epidemiological Surveillance Program, Ministry of Public Health, Ras En Nabaa, Beirut 1107, Lebanon
Nada Ghosn: Epidemiological Surveillance Program, Ministry of Public Health, Ras En Nabaa, Beirut 1107, Lebanon

IJERPH, 2025, vol. 22, issue 11, 1-10

Abstract: During the 2022–2023 cholera outbreak in Lebanon, cases were reported through the District Health Information System 2 (DHIS2). We developed automated procedures in R computing language to improve completeness of routinely notified variables, apply case definition criteria, improve geographic accuracy and documentation of laboratory results. We developed R scripts for data cleaning, standardization, and reclassification, plotted epidemic curves and produced maps to display cholera incidence rates and rapid diagnostic test (RDT) coverage by district. We shared the R scripts on GitHub platform for open adaptation and use. Prior to cleaning, missingness reached 99.7% for inpatient status and 17–35% for other key variables. After cleaning, all fields were complete. Initially, 92.8% of cases were notified through DHIS2 as suspected and 7.2% as confirmed. Following reclassification, 40% were classified as suspected, 5.8% as confirmed, and 48.6% with unspecified classification. Laboratory data revealed that 5.8% of cases were culture positive, 2.2% RDT positive, and 65.1% had no documented testing. Among facility-entered cases (n = 5953), 11.4% were reported from a different governorate than the patient’s residence. At the time of the outbreak, the daily maps were generated based on place of residence. Integrating R-based analytics with DHIS2 enhanced data completeness, improved case classification, and enabled more better spatial and laboratory analysis. This combined approach provided a clearer epidemiological picture of the cholera outbreak, supporting data-driven public health decision-making and highlighting the value of integrating analytical tools with routine surveillance systems.

Keywords: cholera outbreak; surveillance system; DHIS2; Lebanon (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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