Statistical Methods in Reproductive, Maternal, Newborn, Child and Adolescent Health Research
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Probability and Statistics".
Deadline for manuscript submissions: 30 May 2025 | Viewed by 1289
Special Issue Editor
Interests: applied statistics; spatial and spatiotemporal statistics; bayesian modelling and computation; time series analysis; household surveys; administrative data
Special Issue Information
Dear Colleagues,
Improving reproductive, maternal, newborn, child and adolescent health (RMNCAH) is crucial to combating diseases, reducing mortality, eliminating poverty and accelerating progress towards the Sustainable Development Goals (SDGs). Statistical methods play a crucial role in building the evidence base for measuring, monitoring and evaluating progress towards achieving equity in RMNCAH, and contributing to related key policy issues. This Special Issue aims to publish peer-reviewed papers that address the development and application of statistical methods to improve RMNCAH, with a special focus on low and middle-income settings.
Topics to be covered include (but are not limited to): access to healthcare, immunization, infectious diseases, health care in fragile and deprived settings, effect of migration on RMNCAH, evaluation of interventions to improve RMNCAH, spatial, temporal and spatiotemporal analysis of RMNCAH indicators (including mortality and morbidity), machine learning methods, denominator estimation, routine/administrative data analysis and survey design and implementation to estimate RMNCAH indicators.
We encourage papers that address the development of statistical methodology or include novel applications in these areas.
Dr. Chigozie Edson Utazi
Guest Editor
Manuscript Submission Information
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Keywords
- RMNCAH indicator estimation
- access to healthcare
- administrative data analysis
- household survey data
- spatial and spatiotemporal modelling
- machine learning methods
- denominator estimation
- related infectious diseases
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