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Advanced Spatial-Temporal Statistics and Applications for Disease Mapping, Spatial Dependence and Capacity Building in Biostatistics in Sub-Saharan Africa Countries

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Global Health".

Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 15303

Special Issue Editors

Prof. Dr. Ngianga-bakwin Kandala

E-Mail Website
Guest Editor
Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK
Interests: Bayesian Geo-spatial analysis and applications to medicine, Bayesian Geo-Additive Generalized Linear Models, Bayesian Geo-additive discrete-time survival Models/ Survival analysis; Analysis of Discrete Data and State Space Models for Longitudinal and Spatial Data; capacity building in Biostatistics.
Prof. Dr. Samuel Manda

E-Mail Website
Assistant Guest Editor
Department of Statistics, University of Pretoria, Pretoria, South Africa
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
Dr. Tobias Chirwa

E-Mail Website
Assistant Guest Editor
School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 1619, South Africa
Interests: longitudinal and survival data analysis; spatial analysis and Bayesian analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Thomas N O Achia

E-Mail Website
Assistant Guest Editor
Instititute of Mathematical Sciences, Strathmore University, 00100 Nairobi, Kenya
Interests: Bayesian hierarchical modeling; Spatial statistics; Disease mapping; Predictive modeling

Special Issue Information

Dear Colleagues,

Sub-Saharan Africa faces a high disease burden in communicable diseases and an increasing burden in non-communicable diseases with a strong spatial and temporal structure. More recently, increased funding for research from donor initiatives has generated high-quality household data volumes, but there is a high demand for biostatisticians to analyse these data locally and quickly, resulting in a lack of capacity for advanced data analysis.

Globally, the fields of geographical epidemiology and public health surveillance have benefited from combined advances in hierarchical model building and geographical information systems. Exploring and characterising a variety of spatial patterns of diseases at the disaggregated fine geographical resolution has become possible.

Donor-funded initiatives exist to address the dearth in statistical capacity, but few initiatives have been led by African institutions. The Sub-Saharan African Consortium for Advanced Biostatistics (SSACAB) aims to improve biostatistical capacity in Africa according to the needs identified by African institutions, through collaborative masters and doctoral training in biostatistics.

In this issue, we present work resulting in our effort to build capacity in recent developments in spatial-temporal models that have been used to characterise the spatial and temporal patterns of communicable and non-communicable disease burden in SSA through SSACAB and beyond.

Prof. Samuel Manda
Prof. Dr. Ngianga-bakwin Kandala
Prof. Dr. Tobias Chirwa
Prof. Thomas N O Achia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

17 pages, 2293 KiB  
Article
Bayesian Spatial Modeling of Diabetes and Hypertension: Results from the South Africa General Household Survey
by Ropo E. Ogunsakin and Themba G. Ginindza
Int. J. Environ. Res. Public Health 2022, 19(15), 8886; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19158886 - 22 Jul 2022
Cited by 2 | Viewed by 1689
Abstract
Determining spatial links between disease risk and socio-demographic characteristics is vital in disease management and policymaking. However, data are subject to complexities caused by heterogeneity across host classes and space epidemic processes. This study aims to implement a spatially varying coefficient (SVC) model [...] Read more.
Determining spatial links between disease risk and socio-demographic characteristics is vital in disease management and policymaking. However, data are subject to complexities caused by heterogeneity across host classes and space epidemic processes. This study aims to implement a spatially varying coefficient (SVC) model to account for non-stationarity in the effect of covariates. Using the South Africa general household survey, we study the provincial variation of people living with diabetes and hypertension risk through the SVC model. The people living with diabetes and hypertension risk are modeled using a logistic model that includes spatially unstructured and spatially structured random effects. Spatial smoothness priors for the spatially structured component are employed in modeling, namely, a Gaussian Markov random field (GMRF), a second-order random walk (RW2), and a conditional autoregressive (CAR) model. The SVC model is used to relax the stationarity assumption in which non-linear effects of age are captured through the RW2 and allow the mean effect to vary spatially using a CAR model. Results highlight a non-linear relationship between age and people living with diabetes and hypertension. The SVC models outperform the stationary models. The results suggest significant provincial differences, and the maps provided can guide policymakers in carefully exploiting the available resources for more cost-effective interventions. Full article
(This article belongs to the Special Issue Advanced Spatial-Temporal Statistics and Applications for Disease Mapping, Spatial Dependence and Capacity Building in Biostatistics in Sub-Saharan Africa Countries)
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13 pages, 1012 KiB  
Article
A Spatial Survival Model for Risk Factors of Under-Five Child Mortality in Kenya
by Kilemi Daniel, Nelson Owuor Onyango and Rachel Jelagat Sarguta
Int. J. Environ. Res. Public Health 2022, 19(1), 399; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph19010399 - 30 Dec 2021
Cited by 2 | Viewed by 1944
Abstract
Child mortality is high in Sub-Saharan Africa compared to other regions in the world. In Kenya, the risk of mortality is assumed to vary from county to county due to diversity in socio-economic and even climatic factors. Recently, the country was split into [...] Read more.
Child mortality is high in Sub-Saharan Africa compared to other regions in the world. In Kenya, the risk of mortality is assumed to vary from county to county due to diversity in socio-economic and even climatic factors. Recently, the country was split into 47 different administrative regions called counties, and health care was delegated to those county governments, further aggravating the spatial differences in health care from county to county. The goal of this study is to evaluate the effects of spatial variation in under-five mortality in Kenya. Data from the Kenya Demographic Health Survey (KDHS-2014) consisting the newly introduced counties was used to analyze this risk. Using a spatial Cox Proportional Hazard model, an Intrinsic Conditional Autoregressive Model (ICAR) was fitted to account for the spatial variation among the counties in the country while the Cox model was used to model the risk factors associated with the time to death of a child. Inference regarding the risk factors and the spatial variation was made in a Bayesian setup based on the Markov Chain Monte Carlo (MCMC) technique to provide posterior estimates. The paper indicate the spatial disparities that exist in the country regarding child mortality in Kenya. The specific counties have mortality rates that are county-specific, although neighboring counties have similar hazards for death of a child. Counties in the central Kenya region were shown to have the highest hazard of death, while those from the western region had the lowest hazard of death. Demographic factors such as the sex of the child and sex of the household head, as well as social economic factors, such as the level of education, accounted for the most variation when spatial differences were factored in. The spatial Cox proportional hazard frailty model performed better compared to the non-spatial non-frailty model. These findings can help the country to plan health care interventions at a subnational level and guide social and health policies by ensuring that counties with a higher risk of Under Five Child Mortality (U5CM) are considered differently from counties experiencing a lower risk of death. Full article
(This article belongs to the Special Issue Advanced Spatial-Temporal Statistics and Applications for Disease Mapping, Spatial Dependence and Capacity Building in Biostatistics in Sub-Saharan Africa Countries)
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10 pages, 1095 KiB  
Article
A Comparison of Bayesian Spatial Models for HIV Mapping in South Africa
by Kassahun Abere Ayalew, Samuel Manda and Bo Cai
Int. J. Environ. Res. Public Health 2021, 18(21), 11215; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182111215 - 26 Oct 2021
Cited by 2 | Viewed by 1837
Abstract
Despite making significant progress in tackling its HIV epidemic, South Africa, with 7.7 million people living with HIV, still has the biggest HIV epidemic in the world. The Government, in collaboration with developmental partners and agencies, has been strengthening its responses to the [...] Read more.
Despite making significant progress in tackling its HIV epidemic, South Africa, with 7.7 million people living with HIV, still has the biggest HIV epidemic in the world. The Government, in collaboration with developmental partners and agencies, has been strengthening its responses to the HIV epidemic to better target the delivery of HIV care, treatment strategies and prevention services. Population-based household HIV surveys have, over time, contributed to the country’s efforts in monitoring and understanding the magnitude and heterogeneity of the HIV epidemic. Local-level monitoring of progress made against HIV and AIDS is increasingly needed for decision making. Previous studies have provided evidence of substantial subnational variation in the HIV epidemic. Using HIV prevalence data from the 2016 South African Demographic and Health Survey, we compare three spatial smoothing models, namely, the intrinsically conditionally autoregressive normal, Laplace and skew-t (ICAR-normal, ICAR-Laplace and ICAR-skew-t) in the estimation of the HIV prevalence across 52 districts in South Africa. The parameters of the resulting models are estimated using Bayesian approaches. The skewness parameter for the ICAR-skew-t model was not statistically significant, suggesting the absence of skewness in the HIV prevalence data. Based on the deviance information criterion (DIC) model selection, the ICAR-normal and ICAR-Laplace had DIC values of 291.3 and 315, respectively, which were lower than that of the ICAR-skewed t (348.1). However, based on the model adequacy criterion using the conditional predictive ordinates (CPO), the ICAR-skew-t distribution had the lowest CPO value. Thus, the ICAR-skew-t was the best spatial smoothing model for the estimation of HIV prevalence in our study. Full article
(This article belongs to the Special Issue Advanced Spatial-Temporal Statistics and Applications for Disease Mapping, Spatial Dependence and Capacity Building in Biostatistics in Sub-Saharan Africa Countries)
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15 pages, 1746 KiB  
Article
A Spatial Analysis of COVID-19 in African Countries: Evaluating the Effects of Socio-Economic Vulnerabilities and Neighbouring
by Samuel O. M. Manda, Timotheus Darikwa, Tshifhiwa Nkwenika and Robert Bergquist
Int. J. Environ. Res. Public Health 2021, 18(20), 10783; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph182010783 - 14 Oct 2021
Cited by 4 | Viewed by 3013
Abstract
The ongoing highly contagious coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China, in December 2019, has now become a global public health problem. Using publicly available data from the COVID-19 data repository of Our World in Data, we aimed to investigate [...] Read more.
The ongoing highly contagious coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China, in December 2019, has now become a global public health problem. Using publicly available data from the COVID-19 data repository of Our World in Data, we aimed to investigate the influences of spatial socio-economic vulnerabilities and neighbourliness on the COVID-19 burden in African countries. We analyzed the first wave (January–September 2020) and second wave (October 2020 to May 2021) of the COVID-19 pandemic using spatial statistics regression models. As of 31 May 2021, there was a total of 4,748,948 confirmed COVID-19 cases, with an average, median, and range per country of 101,041, 26,963, and 2191 to 1,665,617, respectively. We found that COVID-19 prevalence in an Africa country was highly dependent on those of neighbouring Africa countries as well as its economic wealth, transparency, and proportion of the population aged 65 or older (p-value < 0.05). Our finding regarding the high COVID-19 burden in countries with better transparency and higher economic wealth is surprising and counterintuitive. We believe this is a reflection on the differences in COVID-19 testing capacity, which is mostly higher in more developed countries, or data modification by less transparent governments. Country-wide integrated COVID suppression strategies such as limiting human mobility from more urbanized to less urbanized countries, as well as an understanding of a county’s social-economic characteristics, could prepare a country to promptly and effectively respond to future outbreaks of highly contagious viral infections such as COVID-19. Full article
(This article belongs to the Special Issue Advanced Spatial-Temporal Statistics and Applications for Disease Mapping, Spatial Dependence and Capacity Building in Biostatistics in Sub-Saharan Africa Countries)
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18 pages, 663 KiB  
Article
Bayesian Spatial Modeling of Anemia among Children under 5 Years in Guinea
by Thierno Souleymane Barry, Oscar Ngesa, Nelson Owuor Onyango and Henry Mwambi
Int. J. Environ. Res. Public Health 2021, 18(12), 6447; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18126447 - 15 Jun 2021
Cited by 4 | Viewed by 2617
Abstract
Anemia is a major public health problem in Africa, affecting an increasing number of children under five years. Guinea is one of the most affected countries. In 2018, the prevalence rate in Guinea was 75% for children under five years. This study sought [...] Read more.
Anemia is a major public health problem in Africa, affecting an increasing number of children under five years. Guinea is one of the most affected countries. In 2018, the prevalence rate in Guinea was 75% for children under five years. This study sought to identify the factors associated with anemia and to map spatial variation of anemia across the eight (8) regions in Guinea for children under five years, which can provide guidance for control programs for the reduction of the disease. Data from the Guinea Multiple Indicator Cluster Survey (MICS5) 2016 was used for this study. A total of 2609 children under five years who had full covariate information were used in the analysis. Spatial binomial logistic regression methodology was undertaken via Bayesian estimation based on Markov chain Monte Carlo (MCMC) using WinBUGS software version 1.4. The findings in this study revealed that 77% of children under five years in Guinea had anemia, and the prevalences in the regions ranged from 70.32% (Conakry) to 83.60% (NZerekore) across the country. After adjusting for non-spatial and spatial random effects in the model, older children (48–59 months) (OR: 0.47, CI [0.29 0.70]) were less likely to be anemic compared to those who are younger (0–11 months). Children whose mothers had completed secondary school or above had a 33% reduced risk of anemia (OR: 0.67, CI [0.49 0.90]), and children from household heads from the Kissi ethnic group are less likely to have anemia than their counterparts whose leaders are from Soussou (OR: 0.48, CI [0.23 0.92]). Full article
(This article belongs to the Special Issue Advanced Spatial-Temporal Statistics and Applications for Disease Mapping, Spatial Dependence and Capacity Building in Biostatistics in Sub-Saharan Africa Countries)
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26 pages, 3283 KiB  
Article
Analysing Normative Influences on the Prevalence of Female Genital Mutilation/Cutting among 0–14 Years Old Girls in Senegal: A Spatial Bayesian Hierarchical Regression Approach
by Ngianga-Bakwin Kandala, Chibuzor Christopher Nnanatu, Glory Atilola, Paul Komba, Lubanzadio Mavatikua, Zhuzhi Moore and Dennis Matanda
Int. J. Environ. Res. Public Health 2021, 18(7), 3822; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18073822 - 06 Apr 2021
Cited by 1 | Viewed by 2415
Abstract
Background: Female genital mutilation/cutting (FGM/C) is a harmful traditional practice affecting the health and rights of women and girls. This has raised global attention on the implementation of strategies to eliminate the practice in conformity with the Sustainable Development Goals (SDGs). A recent [...] Read more.
Background: Female genital mutilation/cutting (FGM/C) is a harmful traditional practice affecting the health and rights of women and girls. This has raised global attention on the implementation of strategies to eliminate the practice in conformity with the Sustainable Development Goals (SDGs). A recent study on the trends of FGM/C among Senegalese women (aged 15–49) which examined how individual- and community-level factors affected the practice, found significant regional variations in the practice. However, the dynamics of the practice among girls (0–14 years old) is not fully understood. This paper attempts to fill this knowledge gap by investigating normative influences in the persistence of the practice among Senegalese girls, identify and map ‘hotspots’. Methods: We do so by using a class of Bayesian hierarchical geospatial modelling approach implemented in R statistical software (R Foundation for Statistical Computing, Vienna, Austria) using R2BayesX package. We employed Markov Chain Monte Carlo (MCMC) techniques for full Bayesian inference, while model fit and complexity assessment utilised deviance information criterion (DIC). Results: We found that a girl’s probability of cutting was higher if her mother was cut, supported FGM/C continuation or believed that the practice was a religious obligation. In addition, living in rural areas and being born to a mother from Diola, Mandingue, Soninke or Poular ethnic group increased a girl’s likelihood of being cut. The hotspots identified included Matam, Tambacounda and Kolda regions. Conclusions: Our findings offer a clearer picture of the dynamics of FGM/C practice among Senegalese girls and prove useful in informing evidence-based intervention policies designed to achieve the abandonment of the practice in Senegal. Full article
(This article belongs to the Special Issue Advanced Spatial-Temporal Statistics and Applications for Disease Mapping, Spatial Dependence and Capacity Building in Biostatistics in Sub-Saharan Africa Countries)
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