Risk factors and spatial patterns of hookworm infection among schoolchildren in a rural area of western Côte d'Ivoire

Abstract

This study is aimed at investigating the risk factors for hookworm infection among schoolchildren in a rural area of western Côte d'Ivoire and predicting and mapping the spatial distribution of infection. We used demographic and socio-economic data from a cross-sectional survey of 6–16-year-old schoolchildren from 56 schools. Infection with hookworm was determined by microscopic examination of stool samples employing the Kato–Katz technique and an ether-concentration method. Environmental data were derived from satellite images and digitised maps. Bayesian variogram models were applied to investigate the variation of hookworm infection in relation to demographic, socio-economic and environmental factors. The overall hookworm infection prevalence, based on the pooled microscopic diagnoses, was 43.3% and ranged from 5.4 to 79.1% in the schools surveyed. Bivariate analyses showed that sex, age, socio-economic status, elevation, rainfall and land cover were significantly associated with the spatial distribution of hookworm infection. The final multivariate spatial model consisted of the covariates age, sex, socio-economic status, elevation and land cover. When assuming non-stationary underlying spatial dependency, the results of the model suggested that spatial correlation depended on the location only marginally. We conclude that, at the current resolution, it seems more reasonable to target interventions based on well-established epidemiologic risk factors, rather than on spatial factors.

Introduction

Infections with hookworm (Ancylostoma duodenale and/or Necator americanus) occur worldwide, with the highest prevalences found in poor areas of the developing world. Important risk factors include the lack of sanitary waste disposal and access to clean water, often coupled with inadequate hygiene practices (de Silva et al., 2003, Hotez et al., 2004). The main public health significance of hookworm disease arises from morbid sequelae, such as poor iron status and iron-deficiency anaemia, which has been shown to have adverse effects on children's growth and cognitive development, school performance, course and outcome of pregnancy and worker productivity (Albonico et al., 1999, Crompton, 2000, Gilgen et al., 2001, Brooker et al., 2004, Ezeamama et al., 2005). It is estimated that 740 million people are infected with hookworms (de Silva et al., 2003) and the global burden might be as high as 22.1 million disability-adjusted life years lost each year (WHO, 2002). An estimated 65,000 people die each year due to hookworm disease (WHO, 2002).

The life cycles of A. duodenale and N. americanus involve transmission to humans either through contact with L3-contaminated soils, with L3 penetrating the skin (both A. duodenale and N. americanus), or when L3 is ingested (A. duodenale). The larvae migrate through the bloodstream to the lungs, move up the respiratory tract to enter the oesophagus and finally reach the small intestine where they mature and mate. An adult female worm produces thousands of eggs each day that exit the body via faeces. Eggs deposited in moist, warm and shaded soil hatch within 24–48 h and then develop into L1. They molt twice to become infective L3 to complete their life cycles (Hotez et al., 2004, Utzinger and Keiser, 2004).

Previous experimental and field research across different settings has established the connection between environmental conditions and hookworm infections (Augustine, 1923, Beaver, 1953, Diesfeld, 1970, Nwosu and Anya, 1980, Ratard et al., 1992, Saathoff et al., 2005). Experimental studies, for example, have demonstrated that hookworm eggs develop best at temperatures of 30 °C, while above 35 °C the larval stages die (Udonsi and Atata, 1987). A recent field study carried out in South Africa found high hookworm prevalences mainly in areas characterised by sandy soils with a clay content of less than 15%, warm temperatures and relatively high precipitation (Mabaso et al., 2003). To date, surprisingly few studies have focused on the spatial heterogeneity of hookworm infection, let alone application of state-of-the-art geo-statistical analyses.

Recently, we presented a geographic information system (GIS) and remote sensing based approach to predict and map the risk of Schistosoma mansoni infection in the region of Man, western Côte d'Ivoire (Raso et al., 2005a). Bayesian-based geo-statistical models were applied to investigate factors responsible for the spatial distribution of the parasite, generating risk maps that can be employed by decision-makers to spatially target schistosomiasis control interventions. Here, we extend our previous work from S. mansoni to hookworm, also using a Bayesian-based geo-statistical modeling approach, which relaxes the assumption of stationarity.