Ascaris lumbricoides Infection and Its Relation to Environmental Factors in the Mbeya Region of Tanzania, a Cross-Sectional, Population-Based Study

Background: With one quarter of the world population infected, the intestinal nematode Ascaris lumbricoides is one of the most common infectious agents, especially in the tropics and sub-tropics. Infection is caused by oral intake of eggs and can cause respiratory and gastrointestinal problems. To identify high risk areas for intervention, it is necessary to understand the effects of climatic, environmental and socio-demographic conditions on A. lumbricoides infection. Methodology: Cross-sectional survey data of 6, 366 study participants in the Mbeya region of South-Western Tanzania were used to analyze associations between remotely sensed environmental data and A. lumbricoides infection. Non-linear associations were accounted for by using fractional polynomial regression, and socio-demographic and sanitary data were included as potential confounders. Principal Findings: The overall prevalence of A. lumbricoides infection was 6.8%. Our final multivariable model revealed a significant non-linear association between rainfall and A. lumbricoides infection with peak prevalences at 1740 mm of mean annual rainfall. Mean annual land surface temperature during the day was linearly modeled and negatively associated with A. lumbricoides infection (odds ratio (OR) = 0.87, 95% confidence interval (CI) = 0.78-0.97). Furthermore, age, which also showed a significant non-linear association (infection maximum at 7.7 years),socio-economic status (OR = 0.82, CI = 0.68-0.97),and latrine coverage around the house (OR = 0.80, CI = 0.67-0.96) remained in the final model. Conclusions: A. lumbricoides infection was associated with environmental, socio-demographic and sanitary factors both in uni-and multivariable analysis. Non-linear analysis with fractional polynomials can improve model fit, resulting in a better understanding of the relationship between environmental conditions and helminth infection, and more precise predictions of high prevalence areas. However, socio-demographic determinants and sanitary conditions should also be considered, especially when planning public health interventions on a smaller scale, such as the community level

Hookworm Infection and Environmental Factors in Mbeya Region, Tanzania: A Cross-sectional, Population-based study.

Hookworm disease is one of the most common infections and cause of a high disease burden in the tropics and subtropics. Remotely sensed ecological data and model-based geostatistics have been used recently to identify areas in need for hookworm control. Cross-sectional interview data and stool samples from 6,375 participants from nine different sites in Mbeya region, south-western Tanzania, were collected as part of a cohort study. Hookworm infection was assessed by microscopy of duplicate Kato-Katz thick smears from one stool sample from each participant. A geographic information system was used to obtain remotely sensed environmental data such as land surface temperature (LST), vegetation cover, rainfall, and elevation, and combine them with hookworm infection data and with socio-demographic and behavioral data. Uni- and multivariable logistic regression was performed on sites separately and on the pooled dataset. Univariable analyses yielded significant associations for all ecological variables. Five ecological variables stayed significant in the final multivariable model: population density (odds ratio (OR) = 0.68; 95% confidence interval (CI) = 0.63-0.73), mean annual vegetation density (OR = 0.11; 95% CI = 0.06-0.18), mean annual LST during the day (OR = 0.81; 95% CI = 0.75-0.88), mean annual LST during the night (OR = 1.54; 95% CI = 1.44-1.64), and latrine coverage in household surroundings (OR = 1.02; 95% CI = 1.01-1.04). Interaction terms revealed substantial differences in associations of hookworm infection with population density, mean annual enhanced vegetation index, and latrine coverage between the two sites with the highest prevalence of infection. This study supports previous findings that remotely sensed data such as vegetation indices, LST, and elevation are strongly associated with hookworm prevalence. However, the results indicate that the influence of environmental conditions can differ substantially within a relatively small geographic area. The use of large-scale associations as a predictive tool on smaller scales is therefore problematic and should be handled with care

Characteristics of the study participants and environmental conditions at their places of residence.

<p>EPG = eggs per gram of feces; EVI = enhanced vegetation index; IQR = inter-quartile range; LST = land surface temperature; N = number of observations; SES = socio-economic status.</p>*<p>Median for continuous and proportion in percent for binary variables; IQR, minimum and maximum values only shown for continuous variables.</p>†<p>According to Montresor, 1998 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002408#pntd.0002408-Montresor1" target="_blank">[21]</a>.</p

Hookworm prevalence in the EMINI study sites (Mbeya region, Tanzania, 2008/2009).

<p>Color shading of Voronoi polygons drawn around each household indicates household prevalence, labels indicate site name and site prevalence.</p

Linear predictions of hookworm infection probabilities for population density, EVI, and latrine coverage.

<p>According to the site-specific models for Kyela and Itaka, adjusted for all variables shown in the site-specific models in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002408#pntd-0002408-t003" target="_blank">Table 3</a>.</p

Characteristics of study participants and environmental conditions at their places of residence in Kyela and Itaka site.

<p>IQR = inter-quartile range; EPG = eggs per gram of feces; EVI = enhanced vegetation index; LST = land surface temperature; N = number of observations; SES = socio-economic status.</p>*<p>Median for continuous and proportion in percent for binary variables; IQR, minimum and maximum values are not given for binary variables.</p>†<p>According to Montresor 1998 <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002408#pntd.0002408-Montresor1" target="_blank">[21]</a>.</p

Multivariable associations of selected ecological and adjustment variables with hookworm infection status^*.

<p>CI = confidence interval; EVI = enhanced vegetation index; LST = land surface temperature; OR = odds ratio; SES = socio-economic status.</p>*<p><a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0002408#s3" target="_blank">Results</a> of logistic regression adjusted for within-household clustering with robust variance estimates with each model containing only those variables for which data are shown in the Table.</p>†<p>Performed on pooled dataset combining all nine sites.</p>‡<p>Moderated model for Kyela and Itaka sites with site-interaction terms for environmental variables.</p>§<p>Missing stratum not shown.</p

Multivariable association of environmental and socio-demographic factors with <i>A. lumbricoides</i> infection using logistic regression with fractional polynomials (n = 6,363).

<p>β =  beta coefficient; OR  =  odds ratio; 95% CI  =  confidence interval; LST  =  land surface temperature; EVI  =  enhanced vegetation index; SES  =  socio economic score, AIC  =  Akaike information criterion; BIC  =  Bayesian information criterion.</p>a)<p>Adjusted for household clustering using Huber/White/Sandwich variance estimates and for study sites.</p>b)<p>Fractional polynomial transformation with two degrees and powers p = 3: β₁x^p+β₂x^p*ln x.</p>c)<p>Fractional polynomial transformation with two degrees and powers p = −0.5: β₁x^p+β₂x^p*ln x.</p>d)<p>Percentage of households with a latrine within one kilometer around the participant's household.</p

<i>A. lumbricoides</i> prevalence in the study sites.

<p>Color coding indicates household prevalence, labels indicate site name and site prevalence. <i>A. lumbricoides</i> infection is strongly clustered both between and within sites.</p

Lowess smoothed plot of hookworm prevalence over age in EMINI participants (Mbeya region, Tanzania 2008/2009).

<p>Prevalence rises sharply from birth to adolescence and reaches a plateau in early adulthood after which it stays relatively stable.</p