A study of migrants in Italy has shown how statistical modelling can help improve the identification of Neglected Tropical Disease (NTD) infections.
NTDs are a group of 21 diseases that disproportionately affect impoverished communities, primarily in tropical regions. As global migration increases, individuals infected with NTDs may arrive in countries where these diseases are not typically found., making early diagnosis and treatment essential.
The research was led by PhD student Jana Purkiss with Dr Emanuele Giorgi from Lancaster Medical School in collaboration with the University of Naples Federico II, the World Health Organization Collaborating Centre for the Diagnosis of Intestinal Helminths and Protozoa.
Their research published in PLOS Neglected Tropical Diseases focussed on soil-transmitted helminth (STH) infections using a case study of migrants in Italy's Campania region.
STH is a type of worm infection caused by different species of roundworms with three types caused by A. lumbricoides, hookworms, and T. trichiura.
The data included 3,830 migrants from 64 countries, with over 87% male and with a median age of 27.
Researchers explored how publicly available data, such as migrants' countries of origin, can be combined with individual-level information collected from screening centres to improve the identification of infected cases using statistical modelling.
Researchers investigated the power of the models in predicting overall STH infections (A. lumbricoides, hookworms, and T. trichiura) in two main scenarios: for individuals from existing and from new countries.
They concluded that in all prediction scenarios, except for predicting T. trichiura infections, the best model includes both individual-level variables and country-level indicators, and that the country-level indicators are a stronger predictor than the individual-level for both A. lumbricoides and overall STH infections.
In Africa, the country of origin with the highest prevalence of NTD is Guinea Bissau with a 25% STH prevalence among migrants. In South-East Asia, the country of origin with the highest prevalence is Bangladesh with 18.6% STH prevalence among migrants.
Jana Purkiss said: "We demonstrate how statistical models can be used to aid the identification of people who may be infected with these parasitic diseases. Our focus is on showing how publicly available information on the country of origin of migrants, can be combined with individual-level information collected from screening centres, to improve the predictive performance in the identification of infected cases.
"A model-based approach, such as the one outlined in this paper, could provide an effective data-driven approach to inform targeted screening which can help to reduce the burden placed on specialist parasitology laboratories."
The paper has been recognised in a PLOS NTD viewpoint article, where experts commended the data-driven approach and suggested refinements to better address infection risks. There are plans for future collaboration to build on this research.
