Spatial-Temporal Methods in Public Health at the Sub-Saharan Africa: Leveraging Available Health Surveys Data
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Public Health Statistics and Risk Assessment".
Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 18719
Special Issue Editors
Interests: Bayesian multivariate spatial statistics methods; complex sample survey analysis methods; Bayesian nonparametric methods for correlated survival statistics
Special Issues, Collections and Topics in MDPI journals
Interests: Din Chen has a PhD in Statistics from the University of Guelph. He is Professor of Biostatistics at the Department of Biostatistics, Gillings School of Global Public Health and a Wallace H. Kuralt Distinguished Professor at the School of Social Work, University of North Carolina at Chapel Hill. His research interests include clinical trial design and analysis, statistical meta-analysis, Bayesian statistics, causal inference and structural equation modelling. He has more than 200 published papers and co-authored/co-edited 27 books in biostatistical methodology and public health applications. He is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute.
Special Issue Information
Dear Colleagues,
Countries in the sub-Saharan Africa (SSA) region rely on evidence generated from the analyses of nationally representative population and household health surveys. For example, Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and HIV/AIDS and Reproductive Health 61 Surveys, and Malaria Indicator Surveys are conducted in most of the countries in the SSA region. A few countries have surveys that capture health measurement, for example, South Africa runs a HIV Prevalence Survey every 4–5 years, in addition to their annual General Household Surveys and the two-yearly annually National Income Dynamics Survey (NIDS). The samples drawn are based on stratified multistage cluster sampling designs, often with an over-sampling of smaller domains such as urban areas or certain regions of a country. However, the utilization of a wealth of these data sources, of a high quality collected at comparatively enormous costs, remains sub-optimal, because optimal analyses of such data demand advanced statistical techniques.
One of the important biostatistical analyses performed on these data sets is the spatial small area smoothing model, which plays an important role in facilitating a geospatial distribution of disease burden and of informing public health policy intervention and response. These surveys have been repeatedly implemented, and often multiple surveys collecting similar information are carried within countries in the region. Biostatistical spatial methods addressing such multiple sources have not been utilised properly in the region. Furthermore, inherit in most surveys, are the problems concerning the potential bias to small area estimates due to non-response, missing data, and self-reporting. An even greater biostatistical challenge in using these data for spatial smoothing is the unrepresentativeness of data at lower subnational levels. Most of these surveys are designed to collect representative data at national and regional levels. However, these issues and concerns are infrequently considered in the analyses, which may potentially subject the small area estimation results to bias. Failure to account properly for these issues in any diseases mapping analyses would result into incorrect estimates to be used to inform public health policies. Whilst statistical methods exist to overcome these problem, these have not been extensively worked through in a coherent manner, or been packaged appropriately.
We invite paper submissions that address these research questions using health surveys in performing spatial modelling to inform public health in the sub-Saharan region. Papers that show innovative and improved methods to leverage the available data for lower level estimates are encouraged.
Prof. Samuel Manda
Prof. Din Chen
Guest Editors
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