Intervention Strategies and Implementation Determinants of Routine Health Data Quality Improvement in Infectious Disease Programs in Low and Middle-Income Countries: A Scoping Review
DOI:
https://doi.org/10.37287/picnhs.v7i1.2295Keywords:
data quality, digital health interventions, health information systems, low- and middle-income countries (LMICs), routine health dataAbstract
Infectious disease surveillance, program monitoring, and policy decision-making in low- and middle-income countries (LMICs) require high-quality routine health data. Data quality gaps continue to undermine the utility of routine health information systems despite large investments. Many existing studies have assessed data quality outcomes independently, but very few have attempted to synthesize how and why intervention strategies and implementation determinants combine to affect sustainable improvements. This scoping review aimed to map intervention strategies designed to improve routine health data quality in infectious disease programs and to identify key implementation determinants shaping their effectiveness across LMIC health system contexts. A scoping review was conducted following PRISMA-ScR guidance. Peer-reviewed studies examining interventions targeting routine health data quality in LMICs were identified through systematic searches of PubMed, Scopus, and ScienceDirect. Eligible studies included quantitative, qualitative, mixed-methods, and program evaluation designs reporting at least one data quality dimension. Data were synthesized using narrative and thematic analysis to categorize intervention strategies and examine implementation determinants across health system levels. Twenty-four studies met the inclusion criteria. When the function was analyzed separately by dimension, intervention strategies were grouped into four categories as follows: (1) capacity strengthening; (2) design and implement a digital health system directly linked to the health sector capacity; (3) design and implement data validation and feedback mechanisms; develop governance-level reforms. The best evidence of improvement in completeness, accuracy, and timeliness came from interventions that combined digital technologies with workforce training and supervisor feedback. Effectiveness, however, was largely dictated by implementation determinants: organizational data-use culture, interoperability constraints, human resource stability, infrastructure readiness, and governance alignment. Fragmented systems and parallel reporting mechanisms frequently limited sustainability. Improving routine health data quality in LMIC infectious disease programs extends beyond technical system enhancements and requires alignment among technological, organizational, and behavioral determinants. By integrating intervention typologies with implementation determinants, this review provides a systems-oriented synthesis to inform context-responsive, sustainable strategies for strengthening data quality.
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