Influence of Climate Extremes and Land Use on Fecal Contamination of Shallow Tubewells in Bangladesh

Climate extremes in conjunction with some land use practices are expected to have large impacts on water quality. However, the impacts of land use and climate change on fecal contamination of groundwater has not been well characterized. This work quantifies the influences of extreme weather events and land use practices on <i>Escherichia coli</i> presence and concentration in groundwater from 125 shallow wells, a dominant drinking water resource in rural Bangladesh, monitored over a 17 month period. The results showed that <i>E. coli</i> presence was significantly associated with the number of heavy rain days, developed land and areas with more surface water. These variables also had significant impacts on <i>E. coli</i> concentration, with risk ratios of 1.38 (95% CI = 1.16, 1.65), 1.07 (95% CI: 1.05, 1.09), and 1.02 (95% CI = 1.01, 1.03), respectively. Significant synergistic effects on <i>E. coli</i> presence and concentration were observed when land use and weather variables were combined. The findings suggest that climate extremes and land use practices, particularly urbanization, might promote fecal contamination of shallow well water, thus increasing the risk of diarrheal diseases

ZIP growth curve estimates for cholera trajectories.

<p>ZIP growth curve estimates for cholera trajectories.</p

Variables included in the Principle Component Analysis (PCA) to create SES Indices.

<p>Variables included in the Principle Component Analysis (PCA) to create SES Indices.</p

Number of cholera cases, Matlab, Bangladesh, 1983–2007.

<p>Stacked bar chart indicating the number of cholera cases by biotype (Classical, El Tor and O139) between 1983 and 2007 in Matlab, Bangladesh. Red bars indicate number of Classical cholera cases, grey bars indicate the number of El Tor cases, and the black bars indicate the number of O139 cases.</p

Risk of cholera and protective efficacy of killed oral cholera vaccines, by level of vaccine coverage in <i>bari</i>-level social networks.

<p>**p<0.01 for the difference in risk between vaccinees and placebo recipients; * p<0.05 for the difference in risk between vaccinees and placebo recipients. Note: Quintile values are as follows: <28.7, 28.7-37.9%, 38.0-44.9%, 45.0-51.8%, >51.8%. GREY bars show vaccine protective efficacy by quintile of vaccine coverage within social networks developed using bari-level kinship connections. The number shown above each bar is the calculated protective efficacy. The RED line indicates the risk of cholera for placebo recipients while the BLUE line indicates the risk of cholera for vaccine recipients by quintile of vaccine coverage within social networks. The numbers contained in the table below the graph indicate the calculated cholera risk for each group. An asterisk (**) indicate that the cholera risk per 1,000 was significantly different between the placebo and vaccine groups (e.g., the confidence intervals for the two calculated rates did not overlap). Quintiles show the proportion of a person's social network that was vaccinated (e.g., for quintile 1, <28.7% of people in an individual's social network were administered the cholera vaccine).</p

Graph network example of <i>bari</i>-level kinship social connections.

<p>A “0” indicates no social connection between two baris while a “1” indicates a kinship-based social connection exists.</p

Predictors of cholera risk in recipients of vaccine or placebo, <i>bari</i>-level social networks.

<p>*Multivariate odds ratio for the cited variable, adjusted for all other variables in the table.</p><p>†Variable was not considered in models 2 and 3 since all individuals were either vaccinated or not in these models.</p

Mean cholera trajectories from Poisson model for different initial SES groups.

<p>Line graph indicating the mean trajectory of cholera cases from the conditional Poisson model only (Model C) for different levels of SES. These trajectories were estimated holding population constant at 40 people per <i>bari</i> and distance from the icddr,b hospital at 5 km. The blue lines model the trajectory of cholera for <i>baris</i> with low initial SES (1 SD below the mean), the black lines model cholera for <i>baris</i> with mean initial SES, and the red lines model cholera for <i>baris</i> with high initial SES (1 SD above the mean). Solid lines indicate trajectories for <i>baris</i> where the level of SES stays constant over the study period. Dotted lines indicate trajectory for <i>baris</i> where the level of SES either increases or decreases over time. The figure legend indicates how SES changes over time.</p

Mean ZIP cholera trajectories for different initial SES groups.

<p>Line graph indicating the mean trajectory of cholera cases from the full ZIP model (Model C) for different levels of SES. These trajectories were estimated holding population constant at 40 people per <i>bari</i> and distance from the icddr,b hospital at 5 km. The blue lines model the trajectory of cholera for <i>baris</i> with low initial SES (1 SD below the mean), the black lines model cholera for <i>baris</i> with mean initial SES, and the red lines model cholera for <i>baris</i> with high initial SES (1 SD above the mean). Solid lines indicate trajectories for <i>baris</i> where the level of SES stays constant over the study period. Dotted lines indicate trajectory for <i>baris</i> where the level of SES either increases or decreases over time. The figure legend indicates how SES changes over time.</p

Cholera rate by SES quartile, Matlab, Bangladesh, 1993–2007.

<p>Line graph indicating the yearly cholera case rate per 1,000 persons in Matlab, Bangladesh by socioeconomic status index quartile. The blue dotted line indicates SES quartile 1 (the lowest SES) the green dashed line indicates SES quartile 2, the black dashed line indicates SES quartile 3 and the red solid line indicates SES quartile 4 (the highest SES).</p