Dataset Information

Optimizing data visualization for reproductive, maternal, newborn, child health, and nutrition (RMNCH&N) policymaking: data visualization preferences and interpretation capacity among decision-makers in Tanzania.

ABSTRACT:

Background

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. 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.

Methods

We 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.

Results

The 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.

Conclusions

Decision-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.

SUBMITTER: Aung T

PROVIDER: S-EPMC6376719 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Optimizing data visualization for reproductive, maternal, newborn, child health, and nutrition (RMNCH&N) policymaking: data visualization preferences and interpretation capacity among decision-makers in Tanzania.

Aung Tricia T Niyeha Debora D Shagihilu Shagihilu S Mpembeni Rose R Kaganda Joyceline J Sheffel Ashley A Heidkamp Rebecca R

Global health research and policy 20190215

<h4>Background</h4>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. 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 t ...[more]

PMID: 30783631

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Dataset Information

Optimizing data visualization for reproductive, maternal, newborn, child health, and nutrition (RMNCH&N) policymaking: data visualization preferences and interpretation capacity among decision-makers in Tanzania.

Background

Methods

Results

Conclusions

Publications

Optimizing data visualization for reproductive, maternal, newborn, child health, and nutrition (RMNCH&N) policymaking: data visualization preferences and interpretation capacity among decision-makers in Tanzania.

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