Micro-scale Spatial Clustering of Cholera Risk Factors in Urban Bangladesh

<div><p>Close interpersonal contact likely drives spatial clustering of cases of cholera and diarrhea, but spatial clustering of risk factors may also drive this pattern. Few studies have focused specifically on how exposures for disease cluster at small spatial scales. Improving our understanding of the micro-scale clustering of risk factors for cholera may help to target interventions and power studies with cluster designs. We selected sets of spatially matched households (matched-sets) near cholera case households between April and October 2013 in a cholera endemic urban neighborhood of Tongi Township in Bangladesh. We collected data on exposures to suspected cholera risk factors at the household and individual level. We used intra-class correlation coefficients (ICCs) to characterize clustering of exposures within matched-sets and households, and assessed if clustering depended on the geographical extent of the matched-sets. Clustering over larger spatial scales was explored by assessing the relationship between matched-sets. We also explored whether different exposures tended to appear together in individuals, households, and matched-sets. Household level exposures, including: drinking municipal supplied water (ICC = 0.97, 95%CI = 0.96, 0.98), type of latrine (ICC = 0.88, 95%CI = 0.71, 1.00), and intermittent access to drinking water (ICC = 0.96, 95%CI = 0.87, 1.00) exhibited strong clustering within matched-sets. As the geographic extent of matched-sets increased, the concordance of exposures within matched-sets decreased. Concordance between matched-sets of exposures related to water supply was elevated at distances of up to approximately 400 meters. Household level hygiene practices were correlated with infrastructure shown to increase cholera risk. Co-occurrence of different individual level exposures appeared to mostly reflect the differing domestic roles of study participants. Strong spatial clustering of exposures at a small spatial scale in a cholera endemic population suggests a possible role for highly targeted interventions. Studies with cluster designs in areas with strong spatial clustering of exposures should increase sample size to account for the correlation of these exposures.</p></div

Co-occurrence of different household level exposures within households and within matched-sets.

<p>Values above one suggested two exposures tend to appear in the same households or matched-set closer to each other (co-occurrence of two exposures), while values less than one suggested two exposures tend to not appear together. Confidence interval was calculated by 1000 bootstrap iterations. Shading of each grid cell indicated the estimates of co-occurrence of different exposures, and the estimates that were statistically significant based on 1000 bootstrap iterations were marked by asteroids. Use of municipal supplied water and use of tubewell water was structurally correlated, thus the result was not shown in the graph and was marked by a #. As a result, when co-occurrence with the use of supplied water was over 1, co-occurrence with the use of tubewell water tended to be under 1. Household level unhygienic practices were correlated with infrastructure that is suspected to increase cholera risk. No pairs of exposures exhibited significant clustering in the same households.</p

Summary statistics of 5 individual level exposures.

<p>The individual level exposures were dichotomized to not exposed vs. exposed.</p

The high risk categories of 10 exposures and the summary statistics.

<p>All household level exposures were dichotomized into high risk and low risk categories based on previous literature.</p

Clustering of exposures beyond the spatial extent of matched-sets.

<p>We visually assessed how between matched-sets concordance for each exposure varied with the distance between centroids of matched-sets using non-parametric locally weighted polynomial regression models (LOESS). The shaded area represented 95% confidence intervals of bootstrapped routine from 1,000 simulations. The first 5 exposures related to water source (Fig 3.1–5) showed decreasing concordance between matched-set over space, suggesting clustering of exposures beyond spatial extent of matched-set.</p

A flow chart outlining the enrolled households and individuals and those eligible for the study.

<p>A) For all household level exposures, only the data from the 171 spatially matched households participated in both household visits were considered. B) For analyses of individual level exposures, only the data from the 867 individuals in spatially matched households were considered.</p

Clustering of individual exposures.

<p>A,B) Intraclass correlation coefficients (ICCs) and 95% confidence intervals of A) household level exposures and B) individual level exposures. All household level exposures except household density and boiling water showed clustering within matched-sets. For individual level exposures, only eating a meal prepared over 2 hours before consumption showed strong clustering. C,D) Linear association between the concordance of C) household level and D) individual level exposures within matched-sets and the spatial extent of matched-sets. Clustering within each matched-set (within matched-set concordance) was the proportion of pairs of households (or individuals) within the matched-set that had the same exposure. The spatial extent of matched-sets was the median distance between any two households within the matched-set. The coefficients and 95% confidence intervals specified the change in concordance per 100 meters increase in the spatial extent of matched-sets. More spatially compact matched-sets tended to have higher concordance in exposures than the larger ones, though the trend was not statistically significant for any exposure. E) Five exposures appeared to show clustering beyond the extent of matched-sets based on visual inspection of LOESS plots in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0004400#pntd.0004400.g003" target="_blank">Fig 3</a>. The coefficients and 95% confidence intervals specified the change in concordance per 100 meters increase in the distance between matched-sets up to the spatial extent of clustering.</p

Sensitivity and specificity of the clinical predictors of pneumonia in diarrheal children having age specific fast breathing.

<p>Sensitivity and specificity of the clinical predictors of pneumonia in diarrheal children having age specific fast breathing.</p

Clinical characteristics of under-five diarrheal children with (cases) and without pneumonia (controls) on admission and their outcome.

<p>Clinical characteristics of under-five diarrheal children with (cases) and without pneumonia (controls) on admission and their outcome.</p

Results of logistic regression analysis to explore the independent predicting factors of pneumonia in diarrheal children having age specific fast breathing.

<p>Results of logistic regression analysis to explore the independent predicting factors of pneumonia in diarrheal children having age specific fast breathing.</p