Divergent spatial patterns in the prevalence of the human immunodeficiency virus (HIV) and syphilis in South African pregnant women

An analysis of the ecological association between the human immunodeficiency virus (HIV) and syphilis was undertaken using joint mapping modelling based on data from South African national HIV and syphilis sentinel surveillance surveys carried out between 2007 and 2009. The syphilis prevalence, taken as proxy for sexual behaviour and increased HIV transmission, was first used with health district-level deprivation and population density as a covariate in a HIV prevalence spatial regression model and, secondly, together with HIV as a bivariate outcome. HIV was more highly prevalent in deprived and populated urban areas than elsewhere, while syphilis had a high prevalence in less deprived and less populated rural areas. Spatially, the HIV prevalence was lowest in the southwestern and highest in the northeastern parts of the country. This was in discordance to the syphilis prevalence, which revealed negative correlations with the HIV prevalence. Considerable variations across the districts remained after adjusting for the contextual covariate factors. Divergent spatial patterns between HIV and syphilis were identified, regarding both observed and unobserved covariate effects. The differing disease-specific spatial prevalence patterns may point to inconsistent successes in interventions between the two diseases. Overall, the results emphasize the need to develop and test plausible aetiological hypotheses relating to ecological correlations and causes of the disease-specific interjectory between the district

A spatial analysis of COVID-19 reported cases in the Gauteng province, South Africa: Identifying wards to be targeted early in future infectious diseases outbreak

The COVID-19 pandemic caused major disruptions and contributed to the loss of livelihoods and income. The pandemic also provided public health and health systems policy shifts towards better promotion and protection in responding to such disasters and emergencies. Due to differing effects of socio-economic infectious disease vulnerabilities and pre-pandemic levels of preparedness for health emergencies, health system strengthening requires targeted and ununiform implementation. We employ spatial statistical methods on the COVID-19 confirmed cases in identifying wards that could be targeted for strengthening health security in the Gauteng Province, South Africa. In this way, the identified high-risk wards would be more effective and prepared to respond to future pandemics and emergencies.Comment: 21 pages, 10 figures and 9 table

The world in your hands: GeoHealth then and now

Infectious diseases transmitted by vectors/intermediate hosts constitute a major part of the economic burden related to public health in the endemic countries of the tropics, which challenges local welfare and hinders development. The World Health Organization, in partnership with pharmaceutical companies, major donors, endemic countries and non-governmental organizations, aims to eliminate the majority of these infections in the near future. To succeed, the ecological requirements and real-time distributions of the causative agents (bacteria, parasites and viruses) and their vectors must not only be known to a high degree of accuracy, but the data must also be updated more rapidly than has so far been the case. Current approaches include data collection through terrestrial capture on site and satellite-generated information. This article provides an update of currently available sources of remotely-sensed data, including specific information on satellite-borne sensors, and how such data can be handled by Geographical Information Systems (GIS). Computers, when equipped with GIS software based on common spatial denominators, can connect remotely-sensed environmental records with terrestrial-captured data and apply spatial statistics in ways uniquely suited to manage control activities in areas where vector-borne infections dominate

How well does the PCA3–incorporated chun nomogram perform in predicting prostate biopsy outcome among South African men?

The incidence of prostate cancer among South African men is just as significant as it is worldwide [1,2]. Although the role of the prostate cancer antigen 3 (PCA3) assay in predicting biopsy outcome has proven beneficial in a South African context [3], the assessment of its role incorporated into a prostate cancer risk calculator has not yet been explored on the continent of Africa. We aimed to assess the performance of the PCA3-incorporated Chun nomogram [4] and to compare its performance with other contemporary risk calculators.http://www.europeanurology.com/article/S0302-2838(13)00139-5hb201

Inter-country COVID-19 contagiousness variation in eight African countries

DATA AVAILABILITY STATEMENT : Publicly available datasets were analyzed in this study. This data can be found here: https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv.The estimates of contiguousness parameters of an epidemic have been used for health-related policy and control measures such as non-pharmaceutical control interventions (NPIs). The estimates have varied by demographics, epidemic phase, and geographical region. Our aim was to estimate four contagiousness parameters: basic reproduction number (R0), contact rate, removal rate, and infectious period of coronavirus disease 2019 (COVID-19) among eight African countries, namely Angola, Botswana, Egypt, Ethiopia, Malawi, Nigeria, South Africa, and Tunisia using Susceptible, Infectious, or Recovered (SIR) epidemic models for the period 1 January 2020 to 31 December 2021. For reference, we also estimated these parameters for three of COVID-19’s most severely affected countries: Brazil, India, and the USA. The basic reproduction number, contact and remove rates, and infectious period ranged from 1.11 to 1.59, 0.53 to 1.0, 0.39 to 0.81; and 1.23 to 2.59 for the eight African countries. For the USA, Brazil, and India these were 1.94, 0.66, 0.34, and 2.94; 1.62, 0.62, 0.38, and 2.62, and 1.55, 0.61, 0.39, and 2.55, respectively. The average COVID-19 related case fatality rate for 8 African countries in this study was estimated to be 2.86%. Contact and removal rates among an affected African population were positively and significantly associated with COVID-19 related deaths (p-value < 0.003). The larger than one estimates of the basic reproductive number in the studies of African countries indicate that COVID-19 was still being transmitted exponentially by the 31 December 2021, though at different rates. The spread was even higher for the three countries with substantial COVID-19 outbreaks. The lower removal rates in the USA, Brazil, and India could be indicative of lower death rates (a proxy for good health systems). Our findings of variation in the estimate of COVID-19 contagiousness parameters imply that countries in the region may implement differential COVID-19 containment measures.The South African Medical Research Council.https://www.frontiersin.org/journals/public-healtham2023Statistic

Accounting for Sampling Weights in the Analysis of Spatial Distributions of Disease Using Health Survey Data, with an Application to Mapping Child Health in Malawi and Mozambique

Funding Information: Acknowledgments: Support from a doctoral Calouste Gulbenkian Foundation grant (135422 to S.R.C.) is acknowledged. Support from the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) (through the project UIDB/00297/2020 (Centro de Matemática e Aplicações) to S.R.C. and F.M.) is acknowledged. Support from the South Africa Medical Research Council (SAMRC) with funds from the National Treasury in terms of the SAMRC’s competitive Intramural Research Fund (SAMRC-RFA-IFF-02-2016 to S.M.) is acknowledged. We also extend thanks to DHS Measure for allowing us to use the 2015-16 MDHS and 2015 IMASIDA datasets for this study. Funding Information: Funding: This work was partially supported through the project of the Centro de Matemática e Aplicações, UID/MAT/00297/2020, financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology). The APC was by supported the New University of Lisbon through the PhD program in Statistics and Risk Management of the FCT Nova Faculty. Funding Information: This work was partially supported through the project of the Centro de Matemática e Aplicações, UID/MAT/00297/2020, financed by the Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology). The APC was by supported the New University of Lisbon through the PhD program in Statistics and Risk Management of the FCT Nova Faculty. Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Most analyses of spatial patterns of disease risk using health survey data fail to adequately account for the complex survey designs. Particularly, the survey sampling weights are often ignored in the analyses. Thus, the estimated spatial distribution of disease risk could be biased and may lead to erroneous policy decisions. This paper aimed to present recent statistical advances in disease-mapping methods that incorporate survey sampling in the estimation of the spatial distribution of disease risk. The methods were then applied to the estimation of the geographical distribution of child malnutrition in Malawi, and child fever and diarrhoea in Mozambique. The estimation of the spatial distributions of the child disease risk was done by Bayesian methods. Accounting for sampling weights resulted in smaller standard errors for the estimated spatial disease risk, which increased the confidence in the conclusions from the findings. The estimated geographical distributions of the child disease risk were similar between the methods. However, the fits of the models to the data, as measured by the deviance information criteria (DIC), were different.publishersversionpublishe

A scoping review of spatial analysis approaches using health survey data in Sub-Saharan Africa

CITATION: Manda, S., Haushona, N. & Bergquist, R. 2020. A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa. International Journal of Environmental Research and Public Health, 17(9). doi:10.3390/ijerph17093070The original publication is available at https://www.mdpi.com/journal/ijerphSpatial 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.https://www.mdpi.com/1660-4601/17/9/3070/htmPublishers versio

Detecting influential data in multivariate survival models

Statistical techniques for detecting influential data are well developed and commonly used in linear regression, and to some extent in linear mixed-effects models. However, even though the application of multivariate survival models is widely undertaken, the development of diagnostic tools for the models has received less attention. In this article, we extend the martingale-based residuals and leverage commonly used in univariate survival regression to derive influence statistics for the multivariate survival model. The performance of the proposed statistic is evaluated by simulation studies. The statistic is illustrated with an analysis of child clustered survival data to identify influential clusters of observations and their effects on the estimate of fixed-effect coefficients.https://www.tandfonline.com/loi/lsta20hj2023Statistic

Editorial : Application of biostatistics and epidemiological methods for cancer research in Sub-Saharan Africa

No abstract available.https://www.frontiersin.org/journals/public-healtham2023Statistic