학술논문

A Comparison of Markov and Mechanistic Models for Soil-Transmitted Helminth Prevalence Projections in the Context of Survey Design.
Document Type
Article
Source
Clinical Infectious Diseases. 2024 Supplement, Vol. 78, pS146-S152. 7p.
Subject
*SOILS
*PUBLIC health surveillance
*SANITATION
*PREDICTION models
*RESEARCH funding
*CROSS infection
*INFECTION control
*DESCRIPTIVE statistics
*HYGIENE
*POPULATION geography
*HELMINTHIASIS
*SURVEYS
*DISEASES
*GEOGRAPHIC information systems
*EPIDEMICS
*DATA analysis software
*PUBLIC health
*INFECTIOUS disease transmission
*DISEASE complications
Language
ISSN
1058-4838
Abstract
Globally, there are over 1 billion people infected with soil-transmitted helminths (STHs), mostly living in marginalized settings with inadequate sanitation in sub-Saharan Africa and Southeast Asia. The World Health Organization recommends an integrated approach to STH morbidity control through improved access to sanitation and hygiene education and the delivery of preventive chemotherapy (PC) to school-age children delivered through schools. Progress of STH control programs is currently estimated using a baseline (pre-PC) school-based prevalence survey and then monitored using periodical school-based prevalence surveys, known as Impact Assessment Surveys (IAS). We investigated whether integrating geostatistical methods with a Markov model or a mechanistic transmission model for projecting prevalence forward in time from baseline can improve IAS design strategies. To do this, we applied these 2 methods to prevalence data collected in Kenya, before evaluating and comparing their performance in accurately informing optimal survey design for a range of IAS sampling designs. We found that, although both approaches performed well, the mechanistic method more accurately projected prevalence over time and provided more accurate information for guiding survey design. Both methods performed less well in areas with persistent STH hotspots where prevalence did not decrease despite multiple rounds of PC. Our findings show that these methods can be useful tools for more efficient and accurate targeting of PC. The general framework built in this paper can also be used for projecting prevalence and informing survey design for other neglected tropical diseases. [ABSTRACT FROM AUTHOR]