Special Issue "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).

Special Issue Editors

Prof. Samuel Manda
Website
Guest Editor
Biostatistics Research Unt, South African Medical Research Council, Pretoria, South Africa
Interests: Methods research concentrates on Bayesian modeling, analysis of survival and longitudinal studies, design, and analysis of health surveys, spatial modeling, and statistical research combination. Application research focuses on health, epidemiology, and health systems in sub-Saharan Africa. Designing clinical trials and impact evaluation of programs and interventions
Prof. Din Chen
Website
Guest Editor
University of North Carolina, Chapel Hill, NC, USA
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

Manuscript Submission Information

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Published Papers (5 papers)

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Research

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Open AccessArticle
Spatial Co-Clustering of Cardiovascular Diseases and Select Risk Factors among Adults in South Africa
Int. J. Environ. Res. Public Health 2020, 17(10), 3583; https://doi.org/10.3390/ijerph17103583 - 20 May 2020
Abstract
Background: Cardiovascular diseases (CVDs) are part of the leading causes of mortality and morbidity in developing countries, including South Africa, where they are a major public health issue. Understanding the joint spatial clustering of CVDs and associated risk factors to determine areas in [...] Read more.
Background: Cardiovascular diseases (CVDs) are part of the leading causes of mortality and morbidity in developing countries, including South Africa, where they are a major public health issue. Understanding the joint spatial clustering of CVDs and associated risk factors to determine areas in need of enhanced integrated interventions would help develop targeted, cost-effective and productive mediations. We estimated joint spatial associations and clustering patterns of 2 CVDs (stroke and heart attack) and 3 risk factors (hypertension, high blood cholesterol (HBC) and smoking) among adults in South Africa. Methods: We used cross-sectional secondary adult (15–64-year olds) health data from the South African Demographic Health Survey 2016. Age and gender standardized disease incidence ratios were analyzed using joint spatial global and local bivariate Moran’s Index statistics. Results: We found significantly positive univariate spatial clustering for stroke (Moran; s Index = 0.128), smoking (0.606) hypertension (0.236) and high blood cholesterol (0.385). Smoking and high blood cholesterol (0.366), smoking and stroke (0.218) and stroke and high blood cholesterol (0.184) were the only bivariate outcomes with significant bivariate clustering. There was a joint stroke-smoking local “hot spots” cluster among four districts in the urban western part of the country (City of Cape Town; Cape Winelands; Overberg and Eden) and a joint “cold spots” cluster in the rural north-western part of the country. Similar joint “hot spots” clustering was found for stroke and high blood cholesterol, which also had “cold spots” cluster in the rural east-central part of the country. Smoking and high blood cholesterol had a “hot spots” cluster among five districts in the urban western part of the country (City of Cape Town; Cape Winelands; Overberg; Eden, and West Coast) and “cold spots” around the rural districts in east-southern parts of the country. Conclusions: Our study showed that districts tended to co-cluster based on the rates of CVDs and risk factors, where higher rates were found in urban places than in rural areas. These findings are suggestive of a more contagious and spatial diffusion process among interdependent districts in urban districts. Urbanization or rurality needs to be considered when intervention initiatives are implemented with more general approaches in rural areas. The finding of “hot spot” co-clusters in urban areas means that integrated intervention programmes aimed at reducing the risk of CVDs and associated risk factors would be cost-effective and more productive. Full article
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Open AccessArticle
Spatial Distribution of HIV Prevalence among Young People in Mozambique
Int. J. Environ. Res. Public Health 2020, 17(3), 885; https://doi.org/10.3390/ijerph17030885 - 31 Jan 2020
Abstract
Mozambique has a high burden of HIV and is currently ranked sixth worldwide for adult prevalence. In Mozambique, HIV prevalence is not uniformly distributed geographically and throughout the population. We investigated the spatial distribution of HIV infection among adolescents and young people in [...] Read more.
Mozambique has a high burden of HIV and is currently ranked sixth worldwide for adult prevalence. In Mozambique, HIV prevalence is not uniformly distributed geographically and throughout the population. We investigated the spatial distribution of HIV infection among adolescents and young people in Mozambique using the 2009 AIDS Indicator Survey (AIS). Generalized geoadditive modeling, combining kriging and additive modeling, was used to study the geographical variability of HIV risk among young people. The nonlinear spatial effect was assessed through radial basis splines. The estimation process was done using two-stage iterative penalized quasi-likelihood within the framework of a mixed-effects model. Our estimation procedure is an extension of the approach by Vandendijck et al., estimating the range (spatial decay) parameter in a binary context. The results revealed the presence of spatial patterns of HIV infection. After controlling for important covariates, the results showed a greater burden of HIV/AIDS in the central and northern regions of the country. Several socio-demographic, biological, and behavioral factors were found to be significantly associated with HIV infection among young people. The findings are important, as they can help health officials and policy makers to design targeted interventions for responding to the HIV epidemic. Full article
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Open AccessArticle
Spatial Modelling to Inform Public Health Based on Health Surveys: Impact of Unsampled Areas at Lower Geographical Scale
Int. J. Environ. Res. Public Health 2020, 17(3), 786; https://doi.org/10.3390/ijerph17030786 - 28 Jan 2020
Abstract
Small area estimation is an important tool to provide area-specific estimates of population characteristics for governmental organizations in the context of education, public health and care. However, many demographic and health surveys are unrepresentative at a small geographical level, as often areas at [...] Read more.
Small area estimation is an important tool to provide area-specific estimates of population characteristics for governmental organizations in the context of education, public health and care. However, many demographic and health surveys are unrepresentative at a small geographical level, as often areas at a lower level are not included in the sample due to financial or logistical reasons. In this paper, we investigated (1) the effect of these unsampled areas on a variety of design-based and hierarchical model-based estimates and (2) the benefits of using auxiliary information in the estimation process by means of an extensive simulation study. The results showed the benefits of hierarchical spatial smoothing models towards obtaining more reliable estimates for areas at the lowest geographical level in case a spatial trend is present in the data. Furthermore, the importance of auxiliary information was highlighted, especially for geographical areas that were not included in the sample. Methods are illustrated on the 2008 Mozambique Poverty and Social Impact Analysis survey, with interest in the district-specific prevalence of school attendance. Full article
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Open AccessArticle
A Spatial Analysis of the Prevalence of Female Genital Mutilation/Cutting among 0–14-Year-Old Girls in Kenya
Int. J. Environ. Res. Public Health 2019, 16(21), 4155; https://doi.org/10.3390/ijerph16214155 - 28 Oct 2019
Cited by 2
Abstract
Female genital mutilation/cutting (FGM/C), also known as female circumcision, is a global public health and human rights problem affecting women and girls. Several concerted efforts to eliminate the practice are underway in several sub-Saharan African countries where the practice is most prevalent. Studies [...] Read more.
Female genital mutilation/cutting (FGM/C), also known as female circumcision, is a global public health and human rights problem affecting women and girls. Several concerted efforts to eliminate the practice are underway in several sub-Saharan African countries where the practice is most prevalent. Studies have reported variations in the practice with some countries experiencing relatively slow decline in prevalence. This study investigates the roles of normative influences and related risk factors (e.g., geographic location) on the persistence of FGM/C among 0–14 years old girls in Kenya. The key objective is to identify and map hotspots (high risk regions). We fitted spatial and spatio-temporal models in a Bayesian hierarchical regression framework on two datasets extracted from successive Kenya Demographic and Health Surveys (KDHS) from 1998 to 2014. The models were implemented in R statistical software using Markov Chain Monte Carlo (MCMC) techniques for parameters estimation, while model fit and assessment employed deviance information criterion (DIC) and effective sample size (ESS). Results showed that daughters of cut women were highly likely to be cut. Also, the likelihood of a girl being cut increased with the proportion of women in the community (1) who were cut (2) who supported FGM/C continuation, and (3) who believed FGM/C was a religious obligation. Other key risk factors included living in the northeastern region; belonging to the Kisii or Somali ethnic groups and being of Muslim background. These findings offered a clearer picture of the dynamics of FGM/C in Kenya and will aid targeted interventions through bespoke policymaking and implementations. Full article
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Review

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Open AccessReview
A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa
Int. J. Environ. Res. Public Health 2020, 17(9), 3070; https://doi.org/10.3390/ijerph17093070 - 28 Apr 2020
Cited by 3
Abstract
Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review [...] Read more.
Spatial analysis has become an increasingly used analytic approach to describe and analyze spatial characteristics of disease burden, but the depth and coverage of its usage for health surveys data in Sub-Saharan Africa are not well known. The objective of this scoping review was to conduct an evaluation of studies using spatial statistics approaches for national health survey data in the SSA region. An organized literature search for studies related to spatial statistics and national health surveys was conducted through PMC, PubMed/Medline, Scopus, NLM Catalog, and Science Direct electronic databases. Of the 4,193 unique articles identified, 153 were included in the final review. Spatial smoothing and prediction methods were predominant (n = 108), followed by spatial description aggregation (n = 25), and spatial autocorrelation and clustering (n = 19). Bayesian statistics methods and lattice data modelling were predominant (n = 108). Most studies focused on malaria and fever (n = 47) followed by health services coverage (n = 38). Only fifteen studies employed nonstandard spatial analyses (e.g., spatial model assessment, joint spatial modelling, accounting for survey design). We recommend that for future spatial analysis using health survey data in the SSA region, there must be an improve recognition and awareness of the potential dangers of a naïve application of spatial statistical methods. We also recommend a wide range of applications using big health data and the future of data science for health systems to monitor and evaluate impacts that are not well understood at local levels. Full article
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