Evaluation of Interventions and Diagnostics of Neglected Tropical Diseases in sub-Saharan Africa
Lead Research Organisation:
Imperial College London
Department Name: Infectious Disease Epidemiology
Abstract
Most of the Neglected Tropical Diseases (NTDs) have little name-recognition in industrialized nations, but together they cause severe disability in the world‘s poorest countries, decreasing productivity by billions of dollars. Both the World Health Organization (WHO) and the US Centers for Disease Control and Prevention (CDC) have recently identified these diseases as ‘targets of opportunity‘ to improve global health. By providing safe and effective drug treatments to individuals, mass drug administration (MDA) can control seven NTDs.
The proposed research aims to: i) evaluate the effect of different MDA-based interventions on the infection prevalence and intensity of two NTDs: schistosomiasis and trachoma, and on the likelihood of their elimination; and ii) evaluate the performance of the diagnostic tools currently used for Monitoring & Evaluation of interventions against these two NTDs. Robust statistical analysis of relevant data will help to optimize the design of future NTD control programmes, and evaluate the impact of current strategies so that a better quality of life for some of the world‘s poorest communities can be achieved. The results of this research will have implications for infections prevalent in the UK, such as genital Chlamydia, partly responsible for infertility in reproductive age women.
The proposed research aims to: i) evaluate the effect of different MDA-based interventions on the infection prevalence and intensity of two NTDs: schistosomiasis and trachoma, and on the likelihood of their elimination; and ii) evaluate the performance of the diagnostic tools currently used for Monitoring & Evaluation of interventions against these two NTDs. Robust statistical analysis of relevant data will help to optimize the design of future NTD control programmes, and evaluate the impact of current strategies so that a better quality of life for some of the world‘s poorest communities can be achieved. The results of this research will have implications for infections prevalent in the UK, such as genital Chlamydia, partly responsible for infertility in reproductive age women.
Technical Summary
The two infectious diseases discussed in this fellowship application (schistosomiasis, and trachoma) are among the most prevalent of the so-called Neglected Tropical Diseases (NTDs), an umbrella term encompassing a group of parasitic, bacterial, and viral infections collectively imposing a similar disease burden to that of malaria and HIV. Decisions on Mass Drug Administration (MDA), estimates of the burden of morbidity, infection prevalence and intensity of infection and evaluation of control measures, all depend on the results from diagnostic tests.
The proposed research aims to use advanced biostatistical analysis to further understanding of the effect upon the prevalence and intensity of schistosomiasis and of the ocular bacteria causing trachoma, and the likelihood of their elimination, of interventions based on MDA, as well as to evaluate the performance of the diagnostic tools currently used for the Monitoring & Evaluation (M&E) of these two infections.
The main data sources to be used are: (i) annual longitudinal surveys from the Monitoring & Evaluation (M&E) National Schistosomiasis Control Programmes in Niger and Tanzania (iii) monthly longitudinal trachoma studies conducted in The Gambia and Tanzania.
Latent Variable models (LVM) will be fitted in these datasets as measurement error is acknowledged and estimated, by making use of the information from multiple indicators while validation of scales is particularly important in resource-constrained settings.
Specific objectives are to: (i) classify people according to different intensities of Schistosoma haematobium infection who experience poor quality of life in Niger at baseline and follow-ups in order to address questions of schistosomiasis morbidity; (ii) estimate changes in sensitivities and specificities of one and two consecutive days of stool samples for the intensity classifications of S. mansoni infection following treatment; (iii) estimate the true intensity of S. mansoni infection pre- and post-treatment in the Tanzanian schistosomiasis control programme so that its impact can be evaluated; (iv) quantify changes in sensitivity and specificity of active trachoma disease for true infection status at follow-up particularly focused on the households and the dynamics of infection and disease; (v) estimate the true prevalence of Chlamydia trachomatis infection at baseline and follow-up in order to address questions of persistence in some infected households.
LVM applied to these detailed datasets, provides a unique opportunity to answer important questions related to human health issues in the tropics as well as a useful illustration to serve as a guide to the careful application of these modelling ideas to other conditions.
The proposed research aims to use advanced biostatistical analysis to further understanding of the effect upon the prevalence and intensity of schistosomiasis and of the ocular bacteria causing trachoma, and the likelihood of their elimination, of interventions based on MDA, as well as to evaluate the performance of the diagnostic tools currently used for the Monitoring & Evaluation (M&E) of these two infections.
The main data sources to be used are: (i) annual longitudinal surveys from the Monitoring & Evaluation (M&E) National Schistosomiasis Control Programmes in Niger and Tanzania (iii) monthly longitudinal trachoma studies conducted in The Gambia and Tanzania.
Latent Variable models (LVM) will be fitted in these datasets as measurement error is acknowledged and estimated, by making use of the information from multiple indicators while validation of scales is particularly important in resource-constrained settings.
Specific objectives are to: (i) classify people according to different intensities of Schistosoma haematobium infection who experience poor quality of life in Niger at baseline and follow-ups in order to address questions of schistosomiasis morbidity; (ii) estimate changes in sensitivities and specificities of one and two consecutive days of stool samples for the intensity classifications of S. mansoni infection following treatment; (iii) estimate the true intensity of S. mansoni infection pre- and post-treatment in the Tanzanian schistosomiasis control programme so that its impact can be evaluated; (iv) quantify changes in sensitivity and specificity of active trachoma disease for true infection status at follow-up particularly focused on the households and the dynamics of infection and disease; (v) estimate the true prevalence of Chlamydia trachomatis infection at baseline and follow-up in order to address questions of persistence in some infected households.
LVM applied to these detailed datasets, provides a unique opportunity to answer important questions related to human health issues in the tropics as well as a useful illustration to serve as a guide to the careful application of these modelling ideas to other conditions.