Factors That Influence Data Use to Improve Health Service Delivery in Low- and Middle-Income Countries

Background: Health service delivery indicators are designed to reveal how well health services meet a community's needs. Effective use of the data can enable targeted improvements in health service delivery. We conducted a systematic review to identify the factors that influence the use of health service delivery indicators to improve delivery of primary health care services in low- and middle-income settings. Methods: We reviewed empirical studies published in 2005 or later that provided evidence on the use of health service delivery data at the primary care level in low- and middle-income countries. We searched Scopus, Medline, the Cochrane Library, and citations of included studies. We also searched the gray literature, using a separate strategy. We extracted information on study design, setting, study population, study objective, key findings, and any identified lessons learned. Results: Twelve studies met the inclusion criteria. This small number of studies suggests there is insufficient evidence to draw reliable conclusions. However, a content analysis identified the following potentially influential factors, which we classified into 3 categories: governance (leadership, participatory monitoring, regular review of data); production of information (presentation of findings, data quality, qualitative data); and health information system resources (electronic health management information systems, organizational structure, training). Contextual factors and performance-based financing were also each found to have a role; however, discussing these as mediating factors may not be practical in terms of promoting data use. Conclusion: Scant evidence exists regarding factors that influence the use of health service delivery indicators to improve delivery of primary health care services in low- and middle-income countries. However, the existing evidence highlights some factors that may have a role in improving data use. Further research may benefit from comparing data use factors across different types of program indicators or using our classification as a framework for field experiments.This research is supported by an Australian Government Research Training Program Fee Offset Scholarship