Project

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Spatio-temporal dynamics, risk factors and burden estimates of neglected tropical diseases

English title Spatio-temporal dynamics, risk factors and burden estimates of neglected tropical diseases
Applicant Vounatsou Penelope
Number 137156
Funding scheme ProDoc
Research institution Swiss Tropical and Public Health Institute
Institution of higher education University of Basel - BS
Main discipline Medical Statistics
Start/End 01.10.2011 - 30.09.2014
Approved amount 156'482.00
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All Disciplines (2)

Discipline
Medical Statistics
Methods of Epidemiology and Preventive Medicine

Keywords (9)

Geostatistics; Spatio-temporal process; Soil transmitted helminths; Neglected Diseases; Africa; Bayesian methods; Geographical mapping; Gaussian process; Markov chain Monte Carlo

Lay Summary (English)

Lead
Lay summary
Background: There is a growing interest in the scientific community, funding agencies and Ministries of Health (MoH) to control and eventually eliminate neglected tropical diseases (NTDs). Helminth (parasitic worm) infections, particularly soil-transmitted helminthiasis, schistosomiasis and food-borne trematodiasis, are the most common NTDs. Indeed, half of the world’s population is at risk of infection with one or several of these helminths and more than a billion people are currently infected. While considerable progress has been made over the past 10-15 years in Asia and the Americas regarding the control of soil-transmitted helminthiasis and schistosomiasis, little has been achieved in Africa. Clearly, there is a need for up-to-date and reliable maps of the geographical distribution of the NTDs and the number of infected individuals, including underlying risk factors, so that interventions can be targeted in a spatially explicit and cost-effective manner. The World Health Organization recommends regular administration of anthelminthic drugs to at-risk populations, and hence maps are required to show the areas where interventions are necessary. While large-scale mapping of schistosomiasis has recently come to fruition, with regard to soil-transmitted helminthiasis, apart from a few local studies, continental high-spatial resolution maps and assessment of the diseases determinants taking into account their complex interrelations to demography, ecology and socio-economy are not available. Empirical maps of disease distribution over large areas are based on compiled historical data. These data are not standardised in terms of age groups surveyed, survey periods, diagnostic techniques employed, disease outcome measures (e.g. morbidity questionnaire vs. direct parasitological tests) and levels of aggregation (survey locations or regional summaries). Additionally, the diseases-environment relation is not uniform over the study region. Bayesian geostatistical models are the state-of-the-art methodology for predicting disease distribution at high spatial scales. However, application of these models is not practical for data collected over large number of locations due to inherent computational challenges.

Goal and specific objectives: The goal of this Research Module, which is linked to a Training Module entitled “SSPH+ PhD Training Program in Public Health”, is to assess the spatio-temporal dynamics of soil-transmitted helminthiasis across Africa. The project will pursue the following interrelated specific objectives: (i) to assess the contribution of climatic, demographic, environmental and socio-economic determinants on the geographical distribution of soil-transmitted helminthiasis in Africa; (ii) to estimate and obtain continent-wide high-spatial resolution maps of the disease burden (transmission risk, number of infected people and burden estimates); (iii) to assess changes of the disease distribution over time and space; and (iv) to generate continent-wide high-spatial resolution maps of soil-transmitted helminthiasis and schistosomiasis co-endemicity.

Methods of investigation: We propose to accomplish these objectives by employing and further developing state-of-the-art Bayesian (a) binomial models for very large non-stationary geostatistical data; (b) variable selection approaches in a geostatistical regression model; (c) mathematical transmission models to standardize age-heterogeneous historical survey data; and (d) shared component geostatistical models for modelling co-endemicity from single-disease independent surveys. Zero-inflated model specifications will be considered for sparse data. The models will be fitted (i) using Markov chain Monte Carlo (MCMC) and reversible jump (RJMCMC) simulation algorithms; and (ii) analyzing spatially structured diseases data obtained from surveys that took place over the past 30 years in Africa, extracted from peer-reviewed literature, MoH reports, national control programmes that are currently available in an open-access global NTD database (GNTD), which was compiled and being updated by the Swiss TPH and partners.

Proposed time frame: October 2011 to September 2014.

Significance: This research will, first, generate up-to-date burden estimates and maps of soil-transmitted helminthiasis and related co-infections at high spatial resolution across Africa. These maps will be of value in gauging needs of control programmes, and as a benchmark for estimating the effectiveness of national control programmes. Second, the research will deepen our understanding of the climatic, demographic, environmental and socio-economic factors and their relative contribution to the transmission of soil-transmitted helminthiasis. Such information is valuable for health planning and resource allocation, not only for morbidity control by means of large-scale administration of drugs, but also reducing exposure and hence controlling transission. Third, the spatio-temporal dynamics of the distribution of soil-transmitted helminthiasis will be determined, including underlying risk factors. Fourth, rigorous data-driven statistical methodologies for modeling the spatio-temporal dynamics of soil-transmitted helminthiasis will be further enhanced, and hence risk factor analysis, mapping and prediction of the soil-transmitted helminthiasis is strengthened, which is relevant for other NTDs. Finally, the GNTD will be expanded by cataloguing historical data pertaining to NTDs in Africa, which is relevant for researchers and disease control managers.
Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
Spatial and temporal distribution of soil-transmitted helminth infection in sub-Saharan Africa: a systematic review and geostatistical meta-analysis
Karagiannis-Voules DA, Biedermann P, Ekpo UF, Garba A, Müller-Langer E, Mathieu E, Midzi N, Mwinzi P, Polderman AM, Raso G, Sacko M, Talla I, Tchuem Tchuenté LA, Touré S, Winkler MS, Utzinger J, Vounatsou P (2014), Spatial and temporal distribution of soil-transmitted helminth infection in sub-Saharan Africa: a systematic review and geostatistical meta-analysis, in Lancet Infectious Diseases , 1-11.
Bayesian Geostatistical Modeling of Leishmaniasis Incidence in Brazil
Karagiannis-Voules Dimitrios-Alexios, Scholte Ronaldo, Guimarães Luiz H., Utzinger Juerg, Vounatsou Penelope (2013), Bayesian Geostatistical Modeling of Leishmaniasis Incidence in Brazil, in PLoS Neglected Tropical Diseases, 7(5), 1-13.
Bayesian geostatistical modelling of soil-transmitted helminth survey data in the People’s Republic of China
Lai YS, Zhou XN, Utzinger J, Vounatsou P (2013), Bayesian geostatistical modelling of soil-transmitted helminth survey data in the People’s Republic of China, in Parasites and Vectors, 6(359), 1-16.
Bayesian Risk Mapping and Model-Based Estimation of Schistosoma haematobium–Schistosoma mansoni Co-Distribution in Côte d’Ivoire
Chammartin F, Houngbedji CA, Hürlimann E, Yapi RB, Silué KD, Soro G, Kouamé FN, N’Goran EK, Utzinger J, Raso G, Vounatsou P, Bayesian Risk Mapping and Model-Based Estimation of Schistosoma haematobium–Schistosoma mansoni Co-Distribution in Côte d’Ivoire, in PLoS Neglected Tropical Diseases.
Geostatistical modelling of soil-transmitted helminth infection in Cambodia: Do socioeconomic factors improve predictions?
Karagiannis-Voules DA, Odermatt P, Biedermann P, Khieu V, Schär F, Muth S, Utzinger J, Vounatsou P, Geostatistical modelling of soil-transmitted helminth infection in Cambodia: Do socioeconomic factors improve predictions?, in Acta Tropica, 2014.

Collaboration

Group / person Country
Types of collaboration
Dr Idrissa Talla, Direction de la Lutte contre la Maladie, Ministère de la Santé Senegal (Africa)
- Publication
Dr Narcis B. Kabatereine, Ministry of Health Uganda (Africa)
- in-depth/constructive exchanges on approaches, methods or results
Prof Thomas Kristensen, University of Copenhagen Denmark (Europe)
- Publication
Dr Amadou Garba, Réseau International Schistosomiases Environnement Aménagements et Lutte, Niamey Niger (Africa)
- Publication
Prof. Eliézer K. N’Goran, UFR Biosciences, Université de Cocody-Abidjan Ivory Coast (Africa)
- Publication
- Exchange of personnel
Dr Els Mathieu, Center for Global Health, Centers for Disease Control and Prevention United States of America (North America)
- Publication
Prof. Louis-Albert Tchuem Tchuenté, Ministry of Health Cameroon (Africa)
- Publication
- Exchange of personnel
Dr Seydou Toure, Programme National de Lutte contre la Schistosomiase, Ministère de la Santé Burkina Faso (Africa)
- Publication
Dr Uwem Ekpo, Head of Department of Biological Sciences Federal University of Agriculture, Abeokuta Nigeria (Africa)
- Publication
- Exchange of personnel
Prof. Alan Gelfand, Institute of Statistics and Decision Sciences United States of America (North America)
- in-depth/constructive exchanges on approaches, methods or results
Prof Nicholas Midzi, College of Health Sciences, University of Zimbabwe Zimbabwe (Africa)
- Publication
Prof. Mamadou S. Traoré, Institut National de Recherche en Santé Publique Mali (Africa)
- Publication
Dr Pauline Mwinzi, Centre for Global Health Research, Kenya Medical Research Institute, Kisumu Kenya (Africa)
- Publication

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
World health Organisation meeting on the Epidemiology of Soil-Transmitted Helminths Talk given at a conference Spatio-temporal modelling of soil transmitted helminth infections in sub-Saharan Africa 17.02.2014 Geneva, Switzerland Utzinger Jürg; Karagiannis-Voules Dimitrios-Alexios; Vounatsou Penelope;
Annual meeting of the “Swiss Society of Tropical Medicine and Parasitology Talk given at a conference Mapping Leishmaniasis in Brazil 31.10.2013 Basel, Switzerland Utzinger Jürg; Vounatsou Penelope; Karagiannis-Voules Dimitrios-Alexios;
Neglected tropical disease (NTD) mapping and surveillance: supporting country programmes and future research directions Talk given at a conference Geostatistical modelling of soil-transmitted helminthes and schistosomiasis 28.05.2013 London, Great Britain and Northern Ireland Vounatsou Penelope;
World health Organisation workshop on Soil-Transmitted Helminths Talk given at a conference An open access Global Neglected Tropical Disease database (www.gntd.org) 01.05.2013 Geneva, Switzerland Utzinger Jürg; Vounatsou Penelope;
Bayesian methods in Biostatistics and Bioinformatics Poster Bayesian Geostatistical Modeling of Large Leishmaniasis Incidence Data in Brazil using INLA 19.12.2012 Barcelona, Spain Utzinger Jürg; Karagiannis-Voules Dimitrios-Alexios; Vounatsou Penelope;


Self-organised

Title Date Place
Course: INLA and SPDEs 28.02.2013 Swiss TPH, Basel, Switzerland

Associated projects

Number Title Start Funding scheme
118379 Development of spatial statistical methodology for the analysis of health demographic surveillance system (DSS) data 01.10.2007 Project funding (Div. I-III)

Abstract

1. Research plan summaryBackground: There is a growing interest in the scientific community, funding agencies and Ministries of Health (MoH) to control and eventually eliminate neglected tropical diseases (NTDs). Helminth (parasitic worm) infections, particularly soil-transmitted helminthiasis, schistosomiasis and food-borne trematodiasis, are the most common NTDs. Indeed, half of the world’s population is at risk of infection with one or several of these helminths and more than a billion people are currently infected. While considerable progress has been made over the past 10-15 years in Asia and the Americas regarding the control of soil-transmitted helminthiasis and schistosomiasis, little has been achieved in Africa. Clearly, there is a need for up-to-date and reliable maps of the geographical distribution of the NTDs and the number of infected individuals, including underlying risk factors, so that interventions can be targeted in a spatially explicit and cost-effective manner. The World Health Organization recommends regular administration of anthelminthic drugs to at-risk populations, and hence maps are required to show the areas where interventions are necessary. While large-scale mapping of schistosomiasis has recently come to fruition, with regard to soil-transmitted helminthiasis, apart from a few local studies, continental high-spatial resolution maps and assessment of the diseases determinants taking into account their complex interrelations to demography, ecology and socio-economy are not available. Empirical maps of disease distribution over large areas are based on compiled historical data. These data are not standardised in terms of age groups surveyed, survey periods, diagnostic techniques employed, disease outcome measures (e.g. morbidity questionnaire vs. direct parasitological tests) and levels of aggregation (survey locations or regional summaries). Additionally, the diseases-environment relation is not uniform over the study region. Bayesian geostatistical models are the state-of-the-art methodology for predicting disease distribution at high spatial scales. However, application of these models is not practical for data collected over large number of locations due to inherent computational challenges.Goal and specific objectives: The goal of this Research Module, which is linked to a Training Module entitled “SSPH+ PhD Training Program in Public Health”, is to assess the spatio-temporal dynamics of soil-transmitted helminthiasis across Africa. The project will pursue the following interrelated specific objectives: (i) to assess the contribution of climatic, demographic, environmental and socio-economic determinants on the geographical distribution of soil-transmitted helminthiasis in Africa; (ii) to estimate and obtain continent-wide high-spatial resolution maps of the disease burden (transmission risk, number of infected people and burden estimates); (iii) to assess changes of the disease distribution over time and space; and (iv) to generate continent-wide high-spatial resolution maps of soil-transmitted helminthiasis and schistosomiasis co-endemicity.Methods of investigation: We propose to accomplish these objectives by employing and further developing state-of-the-art Bayesian (a) binomial models for very large non-stationary geostatistical data; (b) variable selection approaches in a geostatistical regression model; (c) mathematical transmission models to standardize age-heterogeneous historical survey data; and (d) shared component geostatistical models for modelling co-endemicity from single-disease independent surveys. Zero-inflated model specifications will be considered for sparse data. The models will be fitted (i) using Markov chain Monte Carlo (MCMC) and reversible jump (RJMCMC) simulation algorithms; and (ii) analyzing spatially structured diseases data obtained from surveys that took place over the past 30 years in Africa, extracted from peer-reviewed literature, MoH reports, national control programmes that are currently available in an open-access global NTD database (GNTD), which was compiled and being updated by the Swiss TPH and partners.Proposed time frame: September 2011 to August 2014.Significance: This research will, first, generate up-to-date burden estimates and maps of soil-transmitted helminthiasis and related co-infections at high spatial resolution across Africa. These maps will be of value in gauging needs of control programmes, and as a benchmark for estimating the effectiveness of national control programmes. Second, the research will deepen our understanding of the climatic, demographic, environmental and socio-economic factors and their relative contribution to the transmission of soil-transmitted helminthiasis. Such information is valuable for health planning and resource allocation, not only for morbidity control by means of large-scale administration of drugs, but also reducing exposure and hence controlling transission. Third, the spatio-temporal dynamics of the distribution of soil-transmitted helminthiasis will be determined, including underlying risk factors. Fourth, rigorous data-driven statistical methodologies for modeling the spatio-temporal dynamics of soil-transmitted helminthiasis will be further enhanced, and hence risk factor analysis, mapping and prediction of the soil-transmitted helminthiasis is strengthened, which is relevant for other NTDs. Finally, the GNTD will be expanded by cataloguing historical data pertaining to NTDs in Africa, which is relevant for researchers and disease control managers.
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