Research Article: Trichuris trichiura infection and its relation to environmental factors in Mbeya region, Tanzania: A cross-sectional, population-based study

Date Published: April 6, 2017

Publisher: Public Library of Science

Author(s): Kirsi M. Manz, Petra Clowes, Inge Kroidl, Dickens O. Kowuor, Christof Geldmacher, Nyanda E. Ntinginya, Leonard Maboko, Michael Hoelscher, Elmar Saathoff, David Joseph Diemert.


The intestinal nematode Trichuris trichiura is among the most common causes of human infectious disease worldwide. As for other soil-transmitted nematodes, its reproductive success and thus prevalence and intensity of infection in a given area strongly depend on environmental conditions. Characterization of the influence of environmental factors can therefore aid to identify infection hot spots for targeted mass treatment.

We analyzed data from a cross-sectional survey including 6234 participants from nine distinct study sites in Mbeya region, Tanzania. A geographic information system was used to combine remotely sensed and individual data, which were analyzed using uni- and multivariable Poisson regression. Household clustering was accounted for and when necessary, fractional polynomials were used to capture non-linear relationships between T. trichiura infection prevalence and environmental variables.

T. trichiura infection was restricted to the Kyela site, close to Lake Nyasa with only very few cases in the other eight sites. The prevalence of T. trichiura infection in Kyela was 26.6% (95% confidence interval (CI) 23.9 to 29.6%). Multivariable models revealed a positive association of infection with denser vegetation (prevalence ratio (PR) per 0.1 EVI units = 2.12, CI 1.28 to 3.50) and inverse associations with rainfall (PR per 100 mm = 0.54, CI 0.44 to 0.67) and elevation (PR per meter = 0.89, CI 0.86 to 0.93) while adjusting for age and previous worm treatment. Slope of the terrain was modelled non-linearly and also showed a positive association with T. trichiura infection (p-value p<0.001). Higher prevalences of T. trichiura infection were only found in Kyela, a study site characterized by denser vegetation, high rainfall, low elevation and flat terrain. But even within this site, we found significant influences of vegetation density, rainfall, elevation and slope on T. trichiura infection. The inverse association of rainfall with infection in Kyela is likely due to the fact, that rainfall in this site is beyond the optimum conditions for egg development. Our findings demonstrate that use of remotely sensed environmental data can aid to predict high-risk areas for targeted helminth control.

Partial Text

Infections with soil-transmitted helminths, a group of nematodes affecting humans, are among the most common infections worldwide. The most common helminth species are the roundworm (Ascaris lumbricoides), the whipworm (Trichuris trichiura) and the hookworms (the two species Ancylostoma duodenale and Necator americanus). T. trichiura infection can cause diarrhea, malnutrition, growth retardation and anemia, but light infections are commonly asymptomatic [1]. According to recent estimates, T. trichiura accounts for about 465 million infections world-wide [2]. Sub-Saharan Africa is one of the regions still heavily affected by soil-transmitted helminth infections, since their transmission is enhanced by poor hygienic conditions and poverty. Unfortunately, despite the efforts of preventive mass chemotherapy conducted in many sub-Saharan African countries [3], the T. trichiura prevalence there has not recently declined [2, 4]. Indeed, T. trichiura infection seems to be difficult to cure, since the available drugs are not very effective against this helminth infection [5, 6].

Our results show that T. trichiura infection in Kyela site is significantly associated with environmental variables in both uni- and multivariable analysis. Participant age of 5 to 20 years, previous worm treatment and green vegetation showed significant positive associations and were thus retained in the multivariable models. Inverse uni- and multivariable associations were found for rainfall and elevation. Slope of the terrain was modelled non-linearly corresponding to a positive association of infections with increasing slope.




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