Dataset Information

Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana.

ABSTRACT:

Background

Morbidity due to Schistosoma haematobium and hookworm infections is marked in those with intense co-infections by these parasites. The development of a spatial predictive decision-support tool is crucial for targeting the delivery of integrated mass drug administration (MDA) to those most in need. We investigated the co-distribution of S. haematobium and hookworm infection, plus the spatial overlap of infection intensity of both parasites, in Ghana. The aim was to produce maps to assist the planning and evaluation of national parasitic disease control programs.

Methodology/principal findings

A national cross-sectional school-based parasitological survey was conducted in Ghana in 2008, using standardized sampling and parasitological methods. Bayesian geostatistical models were built, including a multinomial regression model for S. haematobium and hookworm mono- and co-infections and zero-inflated Poisson regression models for S. haematobium and hookworm infection intensity as measured by egg counts in urine and stool respectively. The resulting infection intensity maps were overlaid to determine the extent of geographical overlap of S. haematobium and hookworm infection intensity. In Ghana, prevalence of S. haematobium mono-infection was 14.4%, hookworm mono-infection was 3.2%, and S. haematobium and hookworm co-infection was 0.7%. Distance to water bodies was negatively associated with S. haematobium and hookworm co-infections, hookworm mono-infections and S. haematobium infection intensity. Land surface temperature was positively associated with hookworm mono-infections and S. haematobium infection intensity. While high-risk (prevalence >10-20%) of co-infection was predicted in an area around Lake Volta, co-intensity was predicted to be highest in foci within that area.

Conclusions/significance

Our approach, based on the combination of co-infection and co-intensity maps allows the identification of communities at increased risk of severe morbidity and environmental contamination and provides a platform to evaluate progress of control efforts.

SUBMITTER: Soares Magalhaes RJ

PROVIDER: S-EPMC3110174 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature

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Publications

Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana.

Soares Magalhães Ricardo J RJ Biritwum Nana-Kwadwo NK Gyapong John O JO Brooker Simon S Zhang Yaobi Y Blair Lynsey L Fenwick Alan A Clements Archie C A AC

PLoS neglected tropical diseases 20110607 6

<h4>Background</h4>Morbidity due to Schistosoma haematobium and hookworm infections is marked in those with intense co-infections by these parasites. The development of a spatial predictive decision-support tool is crucial for targeting the delivery of integrated mass drug administration (MDA) to those most in need. We investigated the co-distribution of S. haematobium and hookworm infection, plus the spatial overlap of infection intensity of both parasites, in Ghana. The aim was to produce maps ...[more]

PMID: 21666800

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Project description:BackgroundThe acceleration of the control of soil-transmitted helminth (STH) infections in Nigeria, emphasizing preventive chemotherapy, has become imperative in light of the global fight against neglected tropical diseases. Predictive risk maps are an important tool to guide and support control activities.MethodologySTH infection prevalence data were obtained from surveys carried out in 2011 using standard protocols. Data were geo-referenced and collated in a nationwide, geographic information system database. Bayesian geostatistical models with remotely sensed environmental covariates and variable selection procedures were utilized to predict the spatial distribution of STH infections in Nigeria.Principal findingsWe found that hookworm, Ascaris lumbricoides, and Trichuris trichiura infections are endemic in 482 (86.8%), 305 (55.0%), and 55 (9.9%) locations, respectively. Hookworm and A. lumbricoides infection co-exist in 16 states, while the three species are co-endemic in 12 states. Overall, STHs are endemic in 20 of the 36 states of Nigeria, including the Federal Capital Territory of Abuja. The observed prevalence at endemic locations ranged from 1.7% to 51.7% for hookworm, from 1.6% to 77.8% for A. lumbricoides, and from 1.0% to 25.5% for T. trichiura. Model-based predictions ranged from 0.7% to 51.0% for hookworm, from 0.1% to 82.6% for A. lumbricoides, and from 0.0% to 18.5% for T. trichiura. Our models suggest that day land surface temperature and dense vegetation are important predictors of the spatial distribution of STH infection in Nigeria. In 2011, a total of 5.7 million (13.8%) school-aged children were predicted to be infected with STHs in Nigeria. Mass treatment at the local government area level for annual or bi-annual treatment of the school-aged population in Nigeria in 2011, based on World Health Organization prevalence thresholds, were estimated at 10.2 million tablets.Conclusions/significanceThe predictive risk maps and estimated deworming needs presented here will be helpful for escalating the control and spatial targeting of interventions against STH infections in Nigeria.

Project description:There is increasing interest to control or eradicate the major neglected tropical diseases. Accurate modelling of the geographic distributions of parasitic infections will be crucial to this endeavour. We used 664 community level infection prevalence data collated from the published literature in conjunction with eight environmental variables, altitude and population density, and a multivariate Bayesian generalized linear spatial model that allows explicit accounting for spatial autocorrelation and incorporation of uncertainty in input data and model parameters, to construct the first spatially-explicit map describing LF prevalence distribution in Africa. We also ran the best-fit model against predictions made by the HADCM3 and CCCMA climate models for 2050 to predict the likely distributions of LF under future climate and population changes. We show that LF prevalence is strongly influenced by spatial autocorrelation between locations but is only weakly associated with environmental covariates. Infection prevalence, however, is found to be related to variations in population density. All associations with key environmental/demographic variables appear to be complex and non-linear. LF prevalence is predicted to be highly heterogenous across Africa, with high prevalences (>20%) estimated to occur primarily along coastal West and East Africa, and lowest prevalences predicted for the central part of the continent. Error maps, however, indicate a need for further surveys to overcome problems with data scarcity in the latter and other regions. Analysis of future changes in prevalence indicates that population growth rather than climate change per se will represent the dominant factor in the predicted increase/decrease and spread of LF on the continent. We indicate that these results could play an important role in aiding the development of strategies that are best able to achieve the goals of parasite elimination locally and globally in a manner that may also account for the effects of future climate change on parasitic infection.

Project description:Summary The characteristics and determinants of health and disease are often organized in space, reflecting our spatially extended nature. Understanding the influence of such factors requires models capable of capturing spatial relations. Drawing on statistical parametric mapping, a framework for topological inference well established in the realm of neuroimaging, we propose and validate an approach to the spatial analysis of diverse clinical data—GeoSPM—based on differential geometry and random field theory. We evaluate GeoSPM across an extensive array of synthetic simulations encompassing diverse spatial relationships, sampling, and corruption by noise, and demonstrate its application on large-scale data from UK Biobank. GeoSPM is readily interpretable, can be implemented with ease by non-specialists, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is through computational efficiency readily scalable to large datasets. We provide a complete, open-source software implementation. Highlights • A framework for topological inference applicable to diverse clinical data is proposed• Superior robustness to noise and under-sampling is observed compared with kriging• Application to UK Biobank data is demonstrated The bigger picture Many aspects of health and disease are distributed in space, requiring models of topological organization. The complexity of the task, however, makes spatial analysis comparatively rare in medicine. Here, we introduce GeoSPM, a platform for topological inference from clinical data based on a mature mathematical framework—statistical parametric mapping—validated by decades of use in neuroimaging. We provide comprehensive synthetic evaluation of the approach, and illustrate its application on large-scale data from UK Biobank. The interpretability, flexibility, scalability, ease of implementation, robustness to noise and under-sampling, computational efficiency, and provision of principled criteria of statistical significance, provided by our open-source platform should catalyze wider use of spatial analysis across medicine. We present GeoSPM, an approach to the spatial analysis of diverse clinical data that extends a framework for topological inference, well established in neuroimaging, based on differential geometry and random field theory. We evaluate GeoSPM with extensive synthetic simulations, and apply it to large-scale data from UK Biobank. Our approach is readily interpretable, easy to implement, enables flexible modeling of complex spatial relations, exhibits robustness to noise and under-sampling, offers principled criteria of statistical significance, and is scalable to large datasets.

Project description:BackgroundUnder-five mortality (U5M) rates are among the health indicators of utmost importance globally. It is the goal 3 target 2.1 of the Sustainable Development Goals that is expected to be reduced to at least 25 per 1000 livebirths by 2030. Despite a considerable reduction in U5M observed globally, several countries especially those in sub-Saharan Africa (SSA) like Ghana are struggling to meet this target. Evidence-based targeting and utilization of the available limited public health resources are critical for effective design of intervention strategies that will enhance under-five child survival. We aimed to estimate and map U5M risk, with the ultimate goal of identifying communities at high risk where interventions and further research can be targeted.MethodsThe 2014 Ghana Demographic and Health Survey data was used in this study. Geostatistical analyses were conducted on 5884 children residing in 423 geographical clusters. The outcome variable is child survival status (alive or dead). We employed a geostatistical generalised linear mixed model to investigate both measured and unmeasured child specific and spatial risk factors for child survival. We then visualise child mortality by mapping the predictive probability of survival.ResultsOf the total sampled under 5 children, 289 (4.91%) experienced the outcome of interest. Children born as multiple births were at increased risk of mortality with an adjusted odds ratio (aOR) (aOR: 8.2532, 95% CI: [5.2608-12.9477]) compared to singletons. Maternal age increased risk of mortality (aOR: 1.0325, 95% CI: [1.0128-1.0527]). Child's age (aOR: 0.2277, 95% CI: [0.1870-0.2771]) and number of children under 5 within each household (aOR: 0.3166, 95% CI: [0.2614-0.3835]) were shown to have a protective effect. Additionally, mothers with secondary education level (aOR: 0.6258, 95% CI: [0.4298-0.9114]) decreased the risk of U5M. The predicted U5M risk in 2014 was at 5.98%. Substantial residual spatial variations were observed in U5M.ConclusionThe analysis found that multiple births is highly associated with increased U5M in Ghana. The high-resolution maps show areas and communities where interventions and further research for U5M can be prioritised to have health impact.

Project description:BackgroundIn South Asia, hundreds of millions of people are infected with soil-transmitted helminths (Ascaris lumbricoides, hookworm, and Trichuris trichiura). However, high-resolution risk profiles and the estimated number of people infected have yet to be determined. In turn, such information will assist control programs to identify priority areas for allocation of scarce resource for the control of soil-transmitted helminth infection.MethodologyWe pursued a systematic review to identify prevalence surveys pertaining to soil-transmitted helminth infections in four mainland countries (i.e., Bangladesh, India, Nepal, and Pakistan) of South Asia. PubMed and ISI Web of Science were searched from inception to April 25, 2019, without restriction of language, study design, and survey date. We utilized Bayesian geostatistical models to identify environmental and socioeconomic predictors, and to estimate infection risk at high spatial resolution across the study region.Principal findingsA total of 536, 490, and 410 georeferenced surveys were identified for A. lumbricoides, hookworm, and T. trichiura, respectively. We estimate that 361 million people (95% Bayesian credible interval (BCI) 331-395 million), approximately one-quarter of the South Asia population, was infected with at least one soil-transmitted helminth species in 2015. A. lumbricoides was the predominant species. Moderate to high prevalence (>20%) of any soil-transmitted helminth infection was predicted in the northeastern part and some northern areas of the study region, as well as the southern coastal areas of India. The annual treatment needs for the school-age population requiring preventive chemotherapy was estimated at 165 million doses (95% BCI: 146-185 million).Conclusions/significanceOur risk maps provide an overview of the geographic distribution of soil-transmitted helminth infection in four mainland countries of South Asia and highlight the need for up-to-date surveys to accurately evaluate the disease burden in the region.

Project description:BackgroundSoil-transmitted helminth infections affect tens of millions of individuals in the People's Republic of China (P.R. China). There is a need for high-resolution estimates of at-risk areas and number of people infected to enhance spatial targeting of control interventions. However, such information is not yet available for P.R. China.MethodsA geo-referenced database compiling surveys pertaining to soil-transmitted helminthiasis, carried out from 2000 onwards in P.R. China, was established. Bayesian geostatistical models relating the observed survey data with potential climatic, environmental and socioeconomic predictors were developed and used to predict at-risk areas at high spatial resolution. Predictors were extracted from remote sensing and other readily accessible open-source databases. Advanced Bayesian variable selection methods were employed to develop a parsimonious model.ResultsOur results indicate that the prevalence of soil-transmitted helminth infections in P.R. China considerably decreased from 2005 onwards. Yet, some 144 million people were estimated to be infected in 2010. High prevalence (>20%) of the roundworm Ascaris lumbricoides infection was predicted for large areas of Guizhou province, the southern part of Hubei and Sichuan provinces, while the northern part and the south-eastern coastal-line areas of P.R. China had low prevalence (<5%). High infection prevalence (>20%) with hookworm was found in Hainan, the eastern part of Sichuan and the southern part of Yunnan provinces. High infection prevalence (>20%) with the whipworm Trichuris trichiura was found in a few small areas of south P.R. China. Very low prevalence (<0.1%) of hookworm and whipworm infections were predicted for the northern parts of P.R. China.ConclusionsWe present the first model-based estimates for soil-transmitted helminth infections throughout P.R. China at high spatial resolution. Our prediction maps provide useful information for the spatial targeting of soil-transmitted helminthiasis control interventions and for long-term monitoring and surveillance in the frame of enhanced efforts to control and eliminate the public health burden of these parasitic worm infections.

Dataset Information

Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana.

Background

Methodology/principal findings

Conclusions/significance

Publications

Mapping helminth co-infection and co-intensity: geostatistical prediction in ghana.

Similar Datasets

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