Digital Epidemiological Surveillance Data Utilization for Decision-Making in Disease Control: A Mixed-Methods Evaluation in Riau Province
DOI:
10.29303/jppipa.v11i11.13044Published:
2025-11-25Downloads
Abstract
Digital epidemiological surveillance data forms the foundation of evidence-based public health decision-making, yet data availability does not guarantee optimal utilization in managerial processes. This study aims to evaluate the utilization of digital epidemiological surveillance data in decision-making for disease control programs in Riau Province. The study employed a mixed-methods design with a sequential explanatory approach, involving 156 respondents (decision-makers, surveillance officers, and information system managers) from the Provincial Health Office and 8 District/City Health Offices. Quantitative data were collected through structured questionnaires and analyzed using descriptive, bivariate, and multivariate statistics. Qualitative data were collected through 18 in-depth interviews, 6 FGDs, document review, and observation, then analyzed using thematic content analysis. Although 89.7% of respondents reported routine data availability and 87.5% of locations had implemented web-based surveillance systems, utilization for advanced analysis remained limited: spatial analysis (32.7%), resource allocation planning (45.5%), and forecasting (15.4%). Independent predictors of high data utilization were analytical training (AOR=3.42), satisfactory data quality (AOR=2.87), easy accessibility (AOR=2.64), and adequate supervisory support (AOR=2.31). Major problems included analytical capacity gaps (only 34.6% felt capable), information system fragmentation (31.4% integrated), underutilization of digital infrastructure (only 18.6% routinely using dashboards), and a decision-making culture based on experience rather than data. There is a significant gap between digital surveillance infrastructure availability and its utilization for strategic decision-making. Despite technological investments, digital systems function primarily as digitized manual processes rather than enabling advanced analytics.
Keywords:
Data Utilization Digital Surveillance Epidemiological SurveillanceReferences
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