Abstract
BackgroundReproductive, maternal, newborn, child health, and nutrition (RMNCH&N) data is an indispensable tool for program and policy decisions in low- and middle-income countries. However, being equipped with evidence doesn’t necessarily translate to program and policy changes. This study aimed to characterize data visualization interpretation capacity and preferences among RMNCH&N Tanzanian program implementers and policymakers (“decision-makers”) to design more effective approaches towards promoting evidence-based RMNCH&N decisions in Tanzania.MethodsWe conducted 25 semi-structured interviews in Kiswahili with junior, mid-level, and senior RMNCH&N decision-makers working in Tanzanian government institutions. We used snowball sampling to recruit participants with different rank and roles in RMNCH&N decision-making. Using semi-structured interviews, we probed participants on their statistical skills and data use, and asked participants to identify key messages and rank prepared RMNCH&N visualizations. We used a grounded theory approach to organize themes and identify findings.ResultsThe findings suggest that data literacy and statistical skills among RMNCH&N decision-makers in Tanzania varies. Most participants demonstrated awareness of many critical factors that should influence a visualization choice—audience, key message, simplicity—but assessments of data interpretation and preferences suggest that there may be weak knowledge of basic statistics. A majority of decision-makers have not had any statistical training since attending university. There appeared to be some discomfort with interpreting and using visualizations that are not bar charts, pie charts, and maps.ConclusionsDecision-makers must be able to understand and interpret RMNCH&N data they receive to be empowered to act. Addressing inadequate data literacy and presentation skills among decision-makers is vital to bridging gaps between evidence and policymaking. It would be beneficial to host basic data literacy and visualization training for RMNCH&N decision-makers at all levels in Tanzania, and to expand skills on developing key messages from visualizations.
Highlights
Reproductive, maternal, newborn, child health, and nutrition (RMNCH&N) data is an indispensable tool for program and policy decisions in low- and middle-income countries
Over the past few decades, the global health community has advocated for increasing the availability, quality, and use of data to inform program and policy decision-making in low- and middle-income countries (LMICs)
We interviewed 25 decision-makers involved with decisions related to national health strategy, vaccines, nutrition, and reproductive and child health (RCH) programs
Summary
Reproductive, maternal, newborn, child health, and nutrition (RMNCH&N) data is an indispensable tool for program and policy decisions in low- and middle-income countries. Over the past few decades, the global health community has advocated for increasing the availability, quality, and use of data to inform program and policy decision-making in low- and middle-income countries (LMICs). Coined by some as a “data revolution,” this demand for data is driven, in part, by a need to monitor progress against reproductive, maternal, newborn, and child health and nutrition (RMNCH&N) targets in international accountability frameworks and countrylevel strategies [1]. Translating data to decision-making is a recognized challenge in global health [2,3,4]. The World Bank’s Statistical Capacity Indicator, a country-specific composite score that reflects the types and frequency of data collection, does not consider decision-maker data literacy or data use [6]
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