TY - JOUR
T1 - Optimizing data visualization for reproductive, maternal, newborn, child health, and nutrition (RMNCH&N) policymaking
T2 - data visualization preferences and interpretation capacity among decision-makers in Tanzania
AU - Aung, Tricia
AU - Niyeha, Debora
AU - Shagihilu, Shagihilu
AU - Mpembeni, Rose
AU - Kaganda, Joyceline
AU - Sheffel, Ashley
AU - Heidkamp, Rebecca
N1 - Funding Information:
The authors would like to acknowledge Deogratius Malamsha (NBS), Elinzuu Nicodermo Yohana (NBS), and Hereswida Zeswida for their support with data collection, transcription, and translation. Additionally, we appreciate assistance from Khadija I. Yahya-Malima (Tanzania Commission for Science and Technology) and Clement Kihinga (MOHCDGEC) with interpreting study results. We would like to thank Khatib Khatib, Arnold Mwijage, and Shabani Said for taking notes during data collection. We are grateful for initial input on study design from Steve Gesuale, Amanda Makulec, and Jessica Pomerantz. Finally, we wish to acknowledge Global Affairs Canada, NBS, and the NEP Technical Task Team and High-Level Advisory Committee in Tanzania. This work was funded by Grant Number 7059904 on the “National Evaluation Platform Approach for Accountability in Women’s and Children’s Health” from the Department of Global Affairs Canada to the Institute for International Programs at the Johns Hopkins Bloomberg School of Public Health. Anonymized transcripts and data collection tools from the study are available from the authors on reasonable request.
Funding Information:
This work was funded by Grant Number 7059904 on the “National Evaluation Platform Approach for Accountability in Women’s and Children’s Health” from the Department of Global Affairs Canada to the Institute for International Programs at the Johns Hopkins Bloomberg School of Public Health.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Child health
KW - Data visualization
KW - Maternal
KW - Newborn
KW - Nutrition
KW - Policy
KW - Reproductive
KW - Tanzania
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UR - http://www.scopus.com/inward/citedby.url?scp=85078675015&partnerID=8YFLogxK
U2 - 10.1186/s41256-019-0095-1
DO - 10.1186/s41256-019-0095-1
M3 - Article
AN - SCOPUS:85078675015
SN - 2397-0642
VL - 4
JO - Global Health Research and Policy
JF - Global Health Research and Policy
IS - 1
M1 - 4
ER -