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Coliform Bacteria as Indicators of Diarrheal Risk in Household Drinking Water: Systematic Review and Meta-Analysis
Joshua S. Gruber ,
* E-mail: jsgruber@gmail.com
Affiliation Division of Epidemiology, University of California, Berkeley, California, United States of America
Ayse Ercumen,
John M. Colford Jr
Joshua S. Gruber,
Joshua S. Gruber Ayse Ercumen John M. Colford Jr
Current guidelines recommend the use of Escherichia coli (EC) or thermotolerant ("fecal") coliforms (FC) as indicators of fecal contamination in drinking water. Despite their broad use as measures of water quality, there remains limited evidence for an association between EC or FC and diarrheal illness: a previous review found no evidence for a link between diarrhea and these indicators in household drinking water.
We conducted a systematic review and meta-analysis to update the results of the previous review with newly available evidence, to explore differences between EC and FC indicators, and to assess the quality of available evidence.
We searched major databases using broad terms for household water quality and diarrhea. We extracted study characteristics and relative risks (RR) from relevant studies. We pooled RRs using random effects models with inverse variance weighting, and used standard methods to evaluate heterogeneity and publication bias.
We identified 20 relevant studies; 14 studies provided extractable results for meta-analysis. When combining all studies, we found no association between EC or FC and diarrhea (RR 1.26 [95% CI: 0.98, 1.63]). When analyzing EC and FC separately, we found evidence for an association between diarrhea and EC (RR: 1.54 [95% CI: 1.37, 1.74]) but not FC (RR: 1.07 [95% CI: 0.79, 1.45]). Across all studies, we identified several elements of study design and reporting (e.g., timing of outcome and exposure measurement, accounting for correlated outcomes) that could be improved upon in future studies that evaluate the association between drinking water contamination and health.
Our findings, based on a review of the published literature, suggest that these two coliform groups have different associations with diarrhea in household drinking water. Our results support the use of EC as a fecal indicator in household drinking water.
Citation: Gruber JS, Ercumen A, Colford JM Jr (2014) Coliform Bacteria as Indicators of Diarrheal Risk in Household Drinking Water: Systematic Review and Meta-Analysis. PLoS ONE 9(9): e107429. https://doi.org/10.1371/journal.pone.0107429
Editor: Sudha Chaturvedi, Wadsworth Center, United States of America
Received: October 23, 2013; Accepted: June 18, 2014; Published: September 24, 2014
Copyright: © 2014 Gruber et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The authors have no funding or support to report.
Globally, drinking water has been established as a primary transmission pathway for diarrhea pathogens. [1], [2] In industrialized countries, centrally treated drinking water distribution systems have largely eliminated outbreaks of waterborne diseases, such as typhoid fever and cholera. [3] In developing countries, there is a large body of evidence that improving the microbial quality of drinking water by household treatment and safe storage reduces diarrhea. [4]–[6] Yet, evidence directly linking diarrheal illness to measured fecal contamination in drinking water remains inconclusive. [3], [7], [8]
In general, it is not feasible to test water for all known waterborne pathogens to assess whether it is safe for drinking. [2], [9]–[11] Instead, since the early 1900s there has been heavy reliance on fecal indicator organisms as measures of drinking water quality. [1], [12] Current World Health Organization (WHO) guidelines recommend Escherichia coli (EC) and/or thermotolerant ("fecal") coliforms (FC) as indicators of the effectiveness of disinfection processes, and as index organisms for the potential presence of fecal contamination and waterborne pathogens; [2], [10], [12], [13] previous WHO guidelines use categories of EC and FC concentrations to define levels of disease risk from drinking water. [14] While EC are considered the most suitable indicator organism due to their specificity to fecal sources of contamination, FC are also recommended as an acceptable surrogate; this recommendation exists despite the recognition that the FC group includes coliform species of environmental origin, and is therefore not likely specific to fecal contamination. [2], [12], [13] Despite heavy reliance on EC and FC to assess the microbiological safety of drinking water, it has yet to be shown that either of these specific indicators is associated with waterborne illness. [8]
A previous systematic review and meta-analysis evaluated the evidence for the link between household drinking water quality, measured by fecal indicator organisms, and health; [8] the authors found no evidence of an association between diarrhea and indicators of drinking water contamination (EC, FC and fecal streptococci). This review, however, was limited by the availability, size and quality of articles published at the time the search was conducted (2001). [8] Specifically, the previous review included only three relatively small studies that reported using EC, and therefore focused mostly on FC as a measure of water quality. Given the limited number of studies available to the authors, they were also unable to evaluate the performances of EC and FC separately. Since the previous review, several studies have been published using EC as a measure of water quality (n = 6) [15]–[21], motivating our updated review with this newly available evidence. In addition, this larger body of evidence allows us to evaluate the performance of EC and FC separately; we hypothesized that the two coliform groups would have different associations with diarrhea given their different specificities for fecal contamination. Given the widespread use of EC and FC as measures of water quality by researchers and policy makers, and the limited evidence regarding the association of these proxy measures with actual disease outcomes, we believe that our systematic review and meta-analysis will make a significant contribution to the body of knowledge on the performance of indicator organisms in household drinking water, and have implications for their recommended use for future guidelines and research. In addition, based on the findings of our systematic review, we are able to make several recommendations for the design and reporting of future studies that assess the relationship between drinking water contamination and health.
We searched MEDLINE, EMBASE, and Web of Knowledge databases for relevant articles. We used broad search terms for water, water quality and indicator organisms, diarrhea and household (point-of-use) sampling (Table 1). Titles and abstracts from the search were examined, and the full texts of relevant articles were reviewed. The bibliographies of relevant articles identified from full-text reviews were scanned to identify additional relevant studies. Searches were limited to articles published in peer-reviewed journals, in English, Spanish, German or Turkish (languages spoken by the authors). No restrictions were placed on date of publication. The final search was conducted on February 27, 2013. A protocol was not registered for this systematic review.
Table 1. Systematic Review Search Terms. a
Inclusion and Exclusion Criteria
We reviewed articles that reported data on the association between exposure to Escherichia coli (EC) or thermotolerant ("fecal") coliforms (FC) in household drinking water, and the occurrence of diarrhea under non-outbreak conditions; we excluded other, less commonly used indicator organisms (e.g., total coliforms, fecal streptococci). Our inclusion criterion was that the study collected exposure and outcome data at the household level or at the point of use; studies were excluded if they only reported measures of source water quality (e.g., shared wells, distribution system "nodes"), or used ecologic outcome data (e.g., health ministry reports). No further restrictions were placed on: definitions of diarrhea, recall period, study design, age groups, study location (developed/developing country), study setting (urban/rural), or drinking water source (as long as exposure to indicator bacteria was measured at the point of use).
Study Reporting and Data Extraction
For each relevant study two authors (JG and AE) independently extracted basic study characteristics, including year of publication, sample size, age groups, study location, setting and design, indicator organism and enumeration methods, and diarrhea definition and enumeration methods into standardized forms, and resolved any discrepancies. Where possible, a relative risk (RR) such as odds ratio, cumulative incidence ratio, incidence density ratio or prevalence ratio was extracted from relevant studies. When no effect measure was reported by the authors, raw data were extracted to calculate an appropriate RR and confidence interval using standard methods. [22] If authors reported both raw data and unadjusted RRs we confirmed the reported estimates using the raw data; we used the RRs reported by the authors, unless otherwise noted. If authors reported results for both indicators (EC and FC) the results were treated as two separate studies in the meta-analysis. All RRs were extracted such that a value greater than unity indicated increased risk of illness among the group exposed to contaminated water. We also noted whether authors controlled for additional variables (by study design, stratified analysis or regression methods) and whether they accounted for correlation (clustering) of outcomes in their analysis.
Exposure Thresholds
There are multiple ways to define comparison groups in water quality studies. For example, authors often use a threshold to define exposed and non-exposed groups based on indicator organism counts measured in 100 ml samples of water (i.e., exposed: ≥10 EC/100 ml; non-exposed: <10 EC/100 ml; with RRs calculated to compare these two groups); however, threshold levels can vary between studies. Current WHO guidelines recommend that drinking water that is safe for human consumption should have no detectable EC or FC in any 100 ml sample (i.e., a 1 EC/FC threshold); however, there is also evidence from the literature that much higher indicator organism concentrations are required to cause disease. For example, a study conducted in the Philippines on source water quality [23] previously reported a "threshold effect", where no association with diarrhea was found at the 1 EC, 10 EC or 100 EC cutoffs, but an association was found at the 1000 EC cutoff; it is plausible that a similar threshold effect could be present for point-of-use drinking water. Given the limited number of studies included in the previously published systematic review and meta-analysis, the authors made no attempt to differentiate indicator organism-diarrhea associations at different threshold levels. [8] In our review, we attempted to extract RRs for different thresholds at the 1 EC or FC, 10 EC or FC, 100 EC or FC and 1000 EC or FC levels, when the authors reported these data.
Categorical Exposures
As an alternative approach to exposure categorization, previous WHO guidelines defined disease risk from drinking water based on categories of indicator organism counts measured in 100 ml samples of water: 0 EC or FC, safe; 1–10 EC or FC, low risk; 11–100 EC or FC, intermediate risk; 101–1000 EC or FC, high risk;>1000 EC or FC, very high risk. [14] Investigators commonly use these guidelines to define exposure categories in studies of drinking water quality; each category is treated as an independent exposure group, and RRs are calculated for each elevated category relative to the 0 EC or FC exposure group. In our review, we extracted RRs for these categorical exposures to explore the presence of a dose-response (increased disease risk associated with increasing exposure levels) and to evaluate previous WHO guidelines that defined health risk based on these exposure categories. Within studies that evaluated dose-responses, we extracted p-values for tests of trend. In studies that did not report p-values for tests of trend but reported results for different categorical exposure levels, we extracted the appropriate data, where possible, and conducted our own tests of trend using previously published methods that estimate a log-linear dose-response while accounting for correlations between RRs from a single study (glst command: STATA 12, STATA Corp, College Station Texas). [24]
For consistency with the previously published review, we conducted meta-analyses to calculate a summary measure pooled across all studies which provided an extractable RR, combining all EC and FC studies. [8] The previous review was not explicit about which results were used for meta-analyses if data could be extracted at multiple thresholds. In our review, we estimated a summary measure across all studies using the lowest extractable threshold from each study (e.g., if a study reported raw data that could be combined at the 1 EC, 10 EC and 100 EC thresholds, we included the RR calculated for the 1 EC threshold in the main meta-analysis). In addition, we conducted a sub-group analysis to explore the type of indicator organism used in each study as a source of heterogeneity in the main analysis; for this sub-group analysis we stratified studies by indicator used (EC and FC) and re-pooled summary measures separately for each indicator. In a secondary meta-analysis, we explored evidence for a threshold effect in household drinking water (see "Exposure Thresholds" above). For this analysis, we restricted meta-analyses to studies that reported extractable RRs at the 1 EC, 10 EC and 100 EC thresholds, respectively – this analysis was only feasible for studies that reported extractable data for all three threshold levels.
Summary measures were calculated by meta-analysis using random effects models with inverse variance weighting (STATA 12). [25] Heterogeneity across studies was evaluated using the Mantel-Haentzel χ2 test; we considered a p-value on the χ2 statistic <0.2 as evidence of heterogeneity. We used funnel plots to evaluate publication bias; plot asymmetry was interpreted as evidence of "small study bias", for which publication bias is a likely contributor. [26], [27]
Database searches were completed on February 27, 2013, from which we identified 5,801 titles and abstracts; 20 relevant articles were identified for review (Figure 1). Of the 20 relevant articles, 14 presented data in an extractable format (see Methods) and were included in meta-analyses. One article [28] presented results for both EC and FC; these results were treated as separate studies in the meta-analyses (resulting in 15 total studies).
Figure 1. Systematic review flow chart.
Study Characteristics: All Studies
The 20 relevant studies were published between 1977 and 2012 (Table 2). Two studies were conducted in a developed country (Canada), the rest in low-resource settings; no studies were conducted among populations with access to centralized water-treatment and distribution systems. The study settings included a mix of urban/peri-urban (n = 7) and rural (n = 13), and the study populations included a range of age groups (Table 2). The included articles used cohort (n = 14) and case-control (n = 6) study designs (Tables 3 and 4). We identified one randomized control trial [16] that reported data on the direct association between water quality and diarrhea; we considered this study a cohort design for the purposes of the review since water quality was not randomized. We note that, while cohort designs traditionally measure an exposure and then follow a population over time to measure outcomes, the cohort designs we identified in this review measured exposure (EC or FC): (i) simultaneously with outcomes through cross-sectional sampling; (ii) at intervals during ongoing diarrhea surveillance, or; (iii) the timing and analysis of water samples relative to diarrhea measurement was mixed or not clearly reported (Tables 3 and 4). The included case-control studies measured exposure after disease occurrence by design; however one study [19] utilized a nested case control design with risk set sampling in which disease and EC exposure were measured on the same day.
Table 2. Selected Characteristics of Included Studies.
Table 3. Summary of EC Study Exposure and Outcome Measurements.
Table 4. Summary of FC Study Exposure and Outcome Measurements.
Included studies measured EC (n = 9), FC (n = 10), or both (n = 1) in drinking water samples collected at the point of use. Overall, studies varied in their reporting of enumeration and incubation methods, and we could not always confirm the indicator organism enumerated based on the reported information (Tables 3 and 4). In at least one study, authors reported using EC, but a description of their methods suggested they measured FC. [29] With this exception, we relied on the authors' reporting or deferred to the categorization used by the previous review. [8]
Most studies used a definition of diarrhea that is consistent with current recommendations: three or more loose or watery stools in a 24-hour period (Tables 3 and 4). [30], [31] Four studies included the presence of blood or mucus in stool as part of their definition of diarrhea, [15], [17], [18], [32] and two studies [15], [17] reported dysentery as an outcome separate from "general" diarrhea; we did not include dysentery as a separate outcome in our review. Cohort studies in low-resource settings relied primarily on household surveillance and self-reported symptoms (e.g., maternal recall) for outcome assessment, using a range of recall periods (daily, up to one month; Tables 3 and 4); the two Canadian studies relied on monthly calendars maintained in the household and reported by telephone. Case-control studies mostly identified cases through hospitals and clinics, and selected controls from community samples; however, two studies identified cases and controls based on concurrent [19] or prior [33] household diarrhea surveillance conducted by the investigators.
Among the studies identified in our review, eight studies reported results that controlled for potential confounders; the other 12 studies found no evidence for confounding, or did not report attempting to control for confounding (Tables 5 and 6) – we did not differentiate between these studies in meta-analyses. In general, reporting on the independence of observations or methods to account for clustered outcomes was poor (Tables 5 and 6). Only five of the 20 studies reported using methods to account for correlated outcomes (e.g., generalized estimating equations; GEE). In general, the RRs and confidence intervals we calculated from available raw data were consistent with those reported by the authors, whether or not they reported accounting for correlated outcomes. However, in one of these studies, that reported using GEE, we calculated confidence intervals from extracted raw data that were substantially more conservative than those reported by the authors; [15] we included the more conservative confidence intervals for this study in our meta-analyses (Table 5).
Table 5. EC Study Results.
Table 6. FC Study Results.
Disease-Indicator Associations
Tables 5 and 6 summarize results from EC and FC studies, respectively. Among the ten EC studies, eight reported data that quantified the relationship between indicator exposure and diarrhea (Table 5). Of the eight studies with quantitative results, six reported threshold results at the 1 EC/100 ml cutoff [15], [18], [20], [21], [28], [33] and all six reported RRs greater than unity; however, only one study was able to rule random error out as an explanation for their results (Table 5). [15] The remaining two studies, among the eight that quantified a relationship between EC and diarrhea, reported significant relationships between diarrhea and linear increases in exposure to mean log10 EC concentrations; the results were very consistent across these two studies (Table 5). [16], [19] The two studies that did not have extractable, quantifiable results reported that no "association" or "correlation" was found between EC and diarrhea. [17], [34] However, we could not confirm whether the authors' conclusions were based on the size of the effect estimate (i.e., similar to unity), precision (i.e., "non-significant" confidence intervals or p-values), or both.
Among the 11 FC studies (including FC results from Genthe et al. 1997 as separate from their EC results), nine studies reported data that quantified the relationship between indicator exposure and diarrhea (Table 6). Results from these nine FC studies were less consistent than results from EC studies. Using the lowest extractable threshold, five studies reported estimates that suggest increases in the risk of diarrhea with exposure to FC [32], [35]–[38], and four reported effects equivalent to unity or reduced diarrhea risk with exposure to FC. [28], [39]–[41] Henry and Rahim (1990) reported FC effects for two distinct study areas differentiated by "adequate" versus "poor" sanitation infrastructure; while a single summary measure for this study suggested an increased risk of diarrhea with exposure to FC, the results from the two separate study populations were very different (Table 6; FC counts were associated with an increase in illness in the area with improved sanitation and a reduction in illness in the area with unimproved sanitation). Of the two FC studies that did not report extractable data, results were also mixed: one reported no difference in FC isolation from water in case and control households, [29] the other reported that households with diarrhea had higher overall geometric mean FC levels compared to control households. [42]
Summary Measures
For consistency with the previous review, we conducted meta-analyses across all studies (combining both EC and FC studies), using the lowest extractable threshold for each study (Tables 5 and 6). Across all studies, our summary estimate suggests a non-significant increase in diarrhea risk from exposure to drinking water contaminated with EC or FC (RR 1.26 [95% CI: 0.98, 1.63]; Figure 2). However, we also found evidence of significant heterogeneity (Χ2 = 36.13, degrees of freedom (d.f.) = 12, p<0.001), and evidence of publication bias (Figure 3).
Figure 2. Forest plot for all included studies.
Figure 3. Funnel plot for all included studies (EC and FC studies).
Studies are plotted relative to size (SE of Log RR), and reported log relative risk (Log RR); studies higher on the vertical axis are larger, and studies further to the right have larger RRs. The solid vertical line is the log of the summary RR for all studies, and the dotted lines are "pseudo 95% confidence intervals" for the summary measure. The absence of smaller studies with small or null RRs (lower left region of the plot) creates plot asymmetry, and provides evidence of possible publication bias.
In order to explore a potential source of heterogeneity across all studies, we stratified results by indicator organism (EC vs. FC) and re-estimated summary measures in these sub-groups. Across all EC studies, the summary measure suggests a significant association between exposure to EC in household drinking water and diarrhea (RR: 1.54 [95% CI: 1.37, 1.74]); Figure 4). While we found no evidence of heterogeneity across EC studies (Χ2 = 1.65, d.f. = 5, p = 0.90), and minimal evidence of publication bias (Figure 5), we note that the EC summary measure was disproportionately influenced by one study [15] ("% weight", Figure 4). We therefore conducted a sensitivity analysis by repeating the EC analysis without the study by Brown et al. Removing this study did not impact the magnitude of the summary measure, but did reduce the precision of the estimate (RR 1.48 [95% CI: 1.02, 2.15]); Figure S1); there was no evidence of heterogeneity after removing Brown et al. from the analysis (Χ2 = 1.59, d.f. = 4, p = 0.81). Across all FC studies, our summary effect measure suggests no association between FC exposure in drinking water and diarrhea (RR: 1.07 [95% CI: 0.79, 1.45]); Figure 4); we also found evidence of significant heterogeneity (Χ2 = 2.30, d.f. = 6, p = 0.06), and publication bias (Figure 6).
Figure 4. Forest plot for all studies stratified by indicator organism.
Top panel, EC: Escherichia coli; Bottom panel, FC: thermotolerant ("fecal") coliforms.
Figure 5. Funnel plot for EC studies.
Studies are plotted relative to size (SE of Log RR), and reported log relative risk (Log RR); studies higher on the vertical axis are larger, and studies further to the right have larger RRs. The solid vertical line is the log of the summary RR for all studies, and the dotted lines are "pseudo 95% confidence intervals" for the summary measure. Studies on this plot are generally symmetrical, with the exception of one small study with a large RR, providing minimal evidence of publication bias.
Figure 6. Funnel plot for all FC studies.
Threshold Effects
Two EC studies provided extractable data that allowed us to explore threshold effects at three different levels (1 EC, 10 EC, and 100 EC per 100 ml; Table 5) [15], [18]. RRs were calculated by comparing outcomes in the exposed group (≥EC threshold) to outcomes in the control group (<EC threshold). In one study, [15] increasing the threshold from 1EC to 10EC suggested a slight increase in diarrhea risk; there was no further increase in risk when the threshold was increased to 100EC (Table 5). In the other study, increasing the threshold beyond 1EC did not increase the magnitude of the effect estimates (Table 5). [18] We pooled estimates across these two studies, and found no evidence that increasing thresholds beyond WHO drinking water guidelines (1 EC/100 mL) was associated with an increased risk of diarrhea (Figure 7). [2] A third EC study reported finding no threshold effect at 1000 EC/100 ml, but did not report a RR. [19]
Figure 7. Forest plot for two studies with extractable data at multiple thresholds.
Data were pooled across the two studies at the 1 EC/100 ml threshold (top panel), 10 EC/100 ml threshold (middle panel), and 100 EC/100 ml threshold (bottom panel). EC: Escherichia coli.
Categorical Exposure and Dose-Response
Four EC studies reported investigating a dose-response relationship. [15], [18], [19], [21] Only three of these studies evaluated the presence of a dose-response using similar exposure categories (<1 EC (referent), 1–10 EC, 11–100 EC, 100+, or 101–1000 EC and 1000+; Table 5). Of these three studies, one did not report extractable data, but reported finding no evidence of a dose-response effect. [19] Results from the other two studies [15], [18] suggest a qualitative dose response (increased risk of diarrhea with increasing exposure categories; Table 5); formal tests of trend, however, had mixed results (Brown et al: p<0.001 provides evidence of a dose response; Jensen et al: p = 0.26 does not provide evidence of a dose response). A fourth study [21] used different EC exposure categorizations. The results from this study qualitatively suggested increasing risk across 0.1–1.5 EC and 1.6–700 EC exposure categories; however, the authors found no evidence of a linear trend (p = 0.45; Table 5).
We conducted a systematic review and meta-analysis to evaluate the relationship between diarrheal illness and the presence of EC or FC indicators in household drinking water. Consistent with a prior review, [8] we did not find conclusive evidence for an association between contaminated drinking water and diarrhea when EC and FC results were combined (previous review: RR 1.12 [95% CI: 0.85, 1.48]; our review: RR 1.26, [95% CI: 0.98, 1.63]; Figure 2). We also found evidence of significant heterogeneity and publication bias across all studies (Figure 2, Figure 3). After stratifying studies by EC and FC to explore the choice of indicator organism as a source of heterogeneity, we found that EC studies reported consistent effect estimates that suggest an increased risk of diarrhea with exposure to contaminated drinking water with no evidence of heterogeneity (pooled RR 1.54 [95% CI: 1.37, 1.74]; Table 5, Figure 4). In contrast, FC studies reported mixed results with significant heterogeneity and provided minimal evidence of an association with diarrhea (pooled RR 1.07 [95% CI: 0.79, 1.45]; Table 6, Figure 4). We found no evidence that exposure thresholds greater than 1 EC/100 mL (e.g., 10 EC/100 mL, 100 EC/100 mL) were associated with increased diarrheal disease, but this evidence is based on a limited number of studies. Results regarding a dose-response relationship between EC in household drinking water and diarrhea were limited and inconclusive. In addition to the results of the meta-analysis, we identified several elements of study design and reporting that could improve future studies evaluating the relationship between water quality and health.
Implications for Choice of Indicator Organism to Assess Microbiological Safety of Drinking Water
Current guidelines recommend two specific uses for indicator organisms: (i) fecal indicators (index organisms), used to indicate the presence of fecal contamination, potential pathogens, and possible health risk, and; (ii) process indicators used to evaluate the effectiveness of a water treatment process. [1], [2], [13]. While the performance of coliform bacteria as process indicators has been described previously, [2], [43] and is outside the scope of this review, the results from our review have implications for the use of EC and FC as index organisms in household drinking water. Currently, EC is considered the "most suitable" fecal indicator; however, FC is generally recognized as a suitable surrogate. [1], [2], [12], [13] Our findings support the continued use of EC as a fecal indicator (index organism) to assess water quality at the point of use, but do not support the use of FC as a surrogate as it does not demonstrate a clear association with diarrheal disease outcomes.
Our findings that EC and FC perform differently as indicators of diarrhea risk from contaminated drinking water are not surprising. While EC is generally of fecal origin, [10] and likely to be present simultaneously with diarrheagenic pathogens in recently contaminated drinking water, FC are known to include coliforms of environmental origin (e.g., Klebsiella), in addition to EC. [1], [2], [13] The consistency with which FC methods will be specific to human pathogens will therefore vary with the environments in which water samples are being collected. Indeed, our results show that the magnitude and direction of the effect estimates for FC were highly inconsistent (Figure 4, Table 6); even within a single study [37] the authors found highly divergent results using FC in two separate study areas (Table 6). In contrast, the magnitude and direction of effect estimates across the EC studies included in our review were consistent across diverse study regions, and when pooled provided evidence of a significant association with diarrhea; these results are consistent with the higher specificity of EC for fecal contamination, compared to FC (Table 5, Figure 4). The presence of a dose-response between EC and diarrhea could have provided further evidence to support our findings that drinking water contaminated with EC is associated with diarrhea. However, dose-response results from our review were inconclusive, and represent an avenue for future research.
While our summary measures suggest that the presence of EC in household drinking water is significantly associated with diarrhea, it is important to note that only one individual EC study in our review was able to rule out chance as a possible explanation of their findings [15]. It has been demonstrated that indicator levels can vary considerably even over short periods of time in both household and source water, [44], [45] and that pathogens and indicator organisms have inconsistent correlations. [46] The studies in this review relied on intermittent grab samples to classify household water quality. Even daily grab samples likely provided only a crude assessment of a household's exposure to fecal contamination and diarrheagenic pathogens, which likely contributed to the lack of precision in the individual studies included in our review. While the imprecision of results from individual studies supports a previous assertion that fecal indicator organisms are "blunt" tools to characterize water quality, [19] our findings suggest that they have continued utility in health and water research. Indeed, while recent WHO drinking water guidelines have shifted focus from recommendations using specific indicator levels to define water safety to a more integrated approach centered on water safety plans, these recommendations are largely directed towards the management of water distribution systems. [2], [13] In practice, regional organizations and researchers working in areas without access to drinking water distribution systems rely heavily on indicator organisms to assess the risk of disease from drinking water, and to measure the effectiveness of household water treatment and safe storage interventions; this is particularly true in resource poor settings with limited access to advanced laboratory facilities. Our results suggest that EC have continued value in both of these applications.
Threshold Effects for Diarrhea Risk
The findings from our review do not support the existence of a "threshold effect" in contaminated household drinking water. A previous study evaluating the effect of contaminated source water on diarrhea [23] found no evidence of an association between EC and diarrhea at the 1 EC threshold level; however, significant associations with diarrhea were observed at the 1000 EC threshold level. The authors concluded that their findings of a "threshold effect" supported earlier assertions that relaxing drinking water guidelines (beyond the 1 EC threshold) might be acceptable in developing countries. [47] Contrary to these findings, in our review of household drinking water we found consistently elevated risks of diarrhea at the 1 EC threshold, with a significant summary measure when pooled across all studies. These results support current WHO guidelines recommending that all drinking water intended for human consumption contain zero EC/100 ml, regardless of location. [2] Beyond the 1 EC threshold, our results did not suggest increased risk of diarrhea at elevated cutoffs, but this evidence was based on a limited number of studies.
Recommendations for Future Water Quality Studies
The studies included in our review covered several decades, and varied considerably with respect to methodological quality and completeness of reporting. In this section, we highlight several findings from our systematic review that could improve the design and reporting of future studies that seek to evaluate the relationship between water quality and health.
The studies in our systematic review used very consistent definitions of diarrhea, relying primarily on self- or caregiver reporting of diarrhea symptoms (Tables 3 and 4). However, we note that diarrhea recall periods and identification strategies varied greatly (e.g., daily recall, one-month recall, cases presenting to clinics; Tables 3 and 4). Reliance on self-reported diarrhea symptoms as a health measure could have introduced bias into individual studies through two primary mechanisms, which have implications for the use of self-reported diarrhea as an outcome in future studies. First, as with any self-reported, subjective outcome, there is a potential for differential reporting relative to exposure status [48]. If study-specific reporting biases were differential with respect to drinking water contamination, it is possible that the results of individual studies (and therefore our review) could be biased. However, there is no reason to suspect that individuals would be aware of the indicator status of their drinking water when reporting diarrhea symptoms, which would decrease the potential for this bias. Second, longer follow-up periods can bias diarrhea reporting due to poor outcome recall, which could have biased findings towards the null if non-differential with respect to exposure, and/or compounded biases associated with differential reporting in individual studies. [49] The inclusion of objective health outcome measures (e.g., pathogen-specific antibody responses) could improve future studies by reducing reliance on self-reported diarrhea symptoms and their associated biases. [50], [51] In addition, while our review focused exclusively on diarrhea, expanding the range of water-related health outcomes studied and reviewed (e.g., indicators of growth and malnutrition) could also improve our understanding of the health impacts of drinking water contamination beyond gastrointestinal illnesses.
Very few studies in our review were able to establish temporality between exposures (indicator organism) and outcomes (diarrhea), and therefore mostly relied on the assumption that drinking water samples were representative of an exposure period that was relevant to measured disease outcomes. While this assumption might be reasonable, or the only option given the practicalities of field conditions, it does leave open the possibility of reverse causality (i.e., diarrhea caused by other environmental pathways led to increased fecal contamination of drinking water). The design of future water quality studies should attempt to measure exposure in advance of disease outcomes, and all studies should be explicit about the timing of exposure and disease measurements in their reporting. These recommendations would all but preclude the use of "traditional" case-control studies, where cases and controls (i.e., disease status) are identified first, followed by visits to households to collect water samples to ascertain exposure status. Instead, a cohort design that measures exposure to contaminated drinking water first, followed by surveillance visits after an appropriate incubation period to measure health outcomes, is a more suitable study design. Decisions regarding follow-up frequency, recall period, and measures of disease occurrence (i.e., incidence, prevalence) will vary depending on study context and logistics, but should be carefully considered and explicitly reported. [49], [52]
In studies evaluating the relationship between drinking water quality and health, confounding bias is a concern: there are numerous factors that could cause both drinking water contamination and diarrhea in a given study setting. Overall, the studies in our review were not consistent in reporting adjusted results (Tables 5 and 6), and there was no consistent set of factors used across studies when authors did report attempting to control for confounding. Incomplete control of confounding in the included studies could have biased individual study results, and therefore the findings of our review. Future studies should be explicit about their attempts to measure and control for confounding factors. Additionally, several studies did not account for correlated outcomes in their analyses, which could have influenced the precision of summary estimates. However, for EC studies we note the consistency of the direction and magnitude of the associations reported across diverse study locations, and across studies that did and did not adjust for confounding and/or account for clustering. Regardless, reporting on these issues could be improved upon in future studies.
Our systematic review included published, peer-reviewed studies. As with any systematic review, publication bias, where smaller studies with small or null ("non-positive") results are systematically excluded from the published literature, could have impacted our findings. In our review, we found evidence of publication bias among FC studies (smaller studies with smaller effect sizes appear to be absent from the funnel plot; lower left region, Figure 6); however, inclusion of smaller FC studies with RRs at or less than unity would have strengthened our findings of no association between FC and diarrhea. We found minimal evidence to suggest publication bias among EC studies (Figure 5); however, we cannot rule out publication bias as a possible explanation of our findings – the funnel plot suggests that most studies were balanced around the summary measure with the exception of one small study with a large positive finding. Taken together, there is evidence that smaller studies with small ("non-positive") effects sizes are systematically missing from the literature on the relationship between water quality and diarrheal illness.
In our review, we pooled RRs across relevant studies using meta-analysis to investigate the association of fecal indicator measures in drinking water with diarrhea. The limitations of meta-analysis have been discussed [53]; we caution against interpreting the summary measures from our review as "true" underlying causal effects, but rather consider them additional pieces of evidence regarding the relationship between fecal indicators in drinking water and diarrhea. The evidence from our pooled summary measures, taken together with biologic plausibility and the consistency of results across studies, provides evidence to support a relationship between diarrhea and the presence of EC in point-of-use drinking water; we do not, however, find evidence to support an equivalent relationship between diarrhea and FC.
We reviewed the available literature to identify studies that evaluated the relationship between microbial indicators of drinking water quality and diarrhea. We found that studies using Escherichia coli (EC) as an indicator of household drinking water quality reported consistent effect estimates, that when pooled suggested a significant association with increased diarrheal illness. Results from studies using thermotolerant ("fecal") coliforms (FC), on the other hand, were inconsistent, and suggested no association with diarrhea when pooled. In this review, we also note several areas where the design and reporting of the included studies could have been improved, and make recommendations for future studies. The results from our review suggest that EC has value as a fecal indicator organism, but that use of FC should be considered carefully in contexts where an association with diarrheal disease outcomes is important.
Forest plot of EC Studies Excluding Brown et al.
Checklist S1.
PRISMA Checklist for Systematic Reviews and Meta-Analyses.
The authors would like to acknowledge Professor Kara Nelson for generously reviewing this manuscript, and providing key feedback.
Conceived and designed the experiments: JG JC. Performed the experiments: JG AE. Analyzed the data: JG. Contributed reagents/materials/analysis tools: JG AE. Wrote the paper: JG AE JC.
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53. Berk RA, Freedman DA (2003) Statistical assumptions as empirical commitments: Law, Punishment, and Social Control: Essays in Honor of Sheldon Messinger, 2nd edn. Aldine de Gruyter.
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text:
Q: Need to override Search Filter url param (_search) in jgrid the jquery plugin? Hi I have created a user message page with this plugin.
The grid will show the user inbox and the user outbox(sent) messages. here's a little code:
    jQuery(document).ready(function() {

       //...some stuff
       currentURL = function() {

            return 'json_member_mail.php?task='+ currentBox;
                    //where current box is either inbox or outbox
       }

       //... alot of stuff

       myGrid = jQuery("#list").jqGrid({

        url:currentURL(),
        datatype: 'json',
        mtype: 'GET',

        //      even more stuff  ....

       }).jqGrid('navGrid','#pager',
        {
                        the lil stuff....

            search:true,

                        //the dialog...
                 }

         // the dialog form editing whatever...

   );//grid

});//document.ready

so then when the user clicks on outbox something like this happens:
jQuery("#list").jqGrid('setGridParam',{ url:currentURL(), postData:{lrt:lastReloadTime} }).trigger("reloadGrid"); //where current URL has the GET param task=outbox

it working all great until I use my search filters. for example I search for the messages sent from the user 'foo' and it's all good but when I click on outbox it will still try to show me messages sent by the user 'foo' but I want the search filters to be reset.
I tried loading the search dialog on document ready and closing it immediately to get the filter().reset and all that but it doesn't work :the-built-in-search-filter-box
myGrid.trigger('reloadGrid'); has the same behaviour as well
.jqGrid('setGridParam',{    url:currentURL(), postData:{_search:'false'} }).trigger("reloadGrid");

would fix my problem but it won't override the _search param.
any generous suggestions?

A: I recommend you to use no function call as avalue of the url parameter. The value of the parameter will be caclulated only once during the grid initialization. Instead of that you can use
url: 'json_member_mail.php',
postData: {
    task: function() {/* return currentBox based on criterias which you have */},
    lrt: lastReloadTime
}

If needed you can make the property lrt also as a function.
If some property of postData are functions the function will be called on any ajax request. So you can use really actual value of the currentBox on searching, paging, sorting or page reload.
If you need reload jqGrid you will no more need to change url or postData and you can just call trigger("reloadGrid") only.

A: lol, So I made one of those stupid mistakes...
where in the original message I say "// the dialog form editing whatever..." I made a mistake
yeah I didn't even bother to write that part on here so what I had there was:
}).jqGrid('navGrid','#pager',
        {
            edit:false,
            add:false,
            del:false,
            search: true,
            refresh:true,
            refreshtext:"Refresh",
            searchtext:"Search"
        },
        {},//add
        {}, //edit
        {}, //delete
            {},  <---instead of putting search options on this line
        {        <----I was putting it here on this line
            overlay:false,
            closeOnEscape:true,
                        afterShowSearch:... ,
        } /* allow the view dialog to be closed when user press ESC key*/
    );//grid

however I realized that after I fixed my problem with a hack when I saw the overlay:false was not working and there was still a jQuery UI overlay for search...
what I did might be helpful somehow to someone who reads this one day so what I did was:
there is a get parameter nd that is a Unix Timestamp(POSIX) but in miliseconds (javascripts implementation) unlike php and mysql and most unix stuff (which is in seconds). I also sent a timestamp of the last time any of the navigation links where clicked:
lastReloadTime = new Date().getTime() ;
jQuery("#list").jqGrid('setGridParam',{
        url:currentURL(),
        postData:{lrt:lastReloadTime
}

then in my php file that generates the json data:
$searchOK = 1;

if($_GET['_search']=='true') {

    if( isset($_GET['lrt']) ){

    //so here I check to see if the main mail links have been clicked at all
    //from the time of the first page load
    //if not then its all good but if it has check for the time

        if(   (    ($_GET['nd']/1000)-($_GET['lrt']/1000)    ) < 1) $searchOK=0;
    }

    if($searchOK){ //you can do search since it might be going through pages of a search result

redpajama_set_name: RedPajamaStackExchange


text:
How Streaming Video and Audio Work
Step-by-step Streaming
Using streaming media files is as easy as browsing the Web, but there's a lot that goes on behind the scenes to make the process possible:
Using your Web browser, you find a site that features streaming video or audio.
You find the file you want to access, and you click the image, link or embedded player with your mouse.
The Web server hosting the Web page requests the file from the streaming server.
The software on the streaming server breaks the file into pieces and sends them to your computer using real-time protocols.
The browser plugin, standalone player or Flash application on your computer decodes and displays the data as it arrives.
Your computer discards the data.
All of this requires three basic components -- a player, a server and a stream of data that are all compatible with each other.
Creating and distributing a streaming video or audio file requires its own process:
You record a high-quality video or audio file using film or a digital recorder.
You digitize this data by importing it to your computer and, if necessary, converting it with editing software.
If you're creating a streaming video, you make the image size smaller and reduce the frame rate.
A codec on your computer compresses the file and encodes it to the right format.
You upload the file to a server
The server streams the file to users' computers.
Because of advances in home computers and software, it's become easier for people to create their own streaming videos at home. Most people can't afford to purchase and maintain their own streaming servers and instead pay a service provider to host the videos. But the increased availability of streaming video has also created some challenges. One is copyright. It's easier than ever to illegally copy TV shows or other videos and post them on the Web, and legal action from copyright owners has become more common.
Another challenge has to do with royalties. Streaming video has changed the way people watch TV shows and movies, and some actors, writers and other entertainment industry workers claim they aren't being paid as they would for TV broadcasts or theater screenings. In addition, in March 2007, the U.S. Copyright Royalty Board changed its royalty structure, making Internet radio far more expensive to produce than it had been.
In spite of these complications, the world of streaming video and audio continues to grow. In the next few years, Internet TV, Internet radio and other streaming applications may become real competitors against traditional media.
If you'd like to learn more about streaming video, streaming audio and related topics, you'll find lots of resources below.
How Internet Radio Works
How Internet TV Works
How Television Works
How Internet Infrastructure Works
How Bits and Bytes Work
How iTunes Works
How Podcasting Works
How BitTorrent Works
How WiFi Works
How Web Servers Work
HowStuffWorks Videos
Journal of Multimedia
StreamingMedia.com
Streaming Media: University of Wisconsin
Network World: Streaming Media
The Animated Internet: How Streaming Media Works
Adobe. "A Streaming Media Primer." (10/5/2007) http://www.adobe.com/products/aftereffects/pdfs/AdobeStr.pdf
Klass, Brian. "Streaming Media in Higher Education: Possibilities and Pitfalls." 5/30/2003 (10/5/2007) http://campustechnology.com/articles/38707/
Larson, Lisa. "A Crash Course in Flash Video." StreamingMedia.com. 9/24/2007 (10/5/2007) http://www.streamingmedia.com/article.asp?id=9711&c=8
Media College. "Introduction to How to Create Streaming Video." (10/5/2007) http://www.mediacollege.com/video/streaming/overview.html
Reuters. "YouTube Serves Up to 100 Million Videos a Day." USA Today. 7/16/2006 (10/5/2007). http://www.usatoday.com/tech/news/2006-07-16-youtube-views_x.htm
Steinmetz, Mike. "Streaming Media." (10/5/2007) http://www.digitalwebcast.com/Htm/Tutorials/streaming/streaming.htm
StreamingMedia.com. "Understanding Streaming Media Protocols." 2/2/2003 (10/5/2007) http://www.streamingmedia.com/article.asp?id=8291&c=1
University of Wisconsin. "Understanding Streaming Media." (10/5/2007) http://streaming.wisconsin.edu/understand/understand.html
How to Change the Language in Google Chrome
How Google Works
The World Wide Web Turns 30!

redpajama_set_name: RedPajamaCommonCrawl


The citation from the Academy of Motion Picture Arts and Sciences reads: "...to Rosco Laboratories for the development of an improved non-toxic fluid for creating fog and smoke for motion picture production."
Rosco Fog Fluid produces virtually any kind of fog effect easily and continuously. It is ideal for use in theater, film, television, live entertainment and special events.
Developed almost 30 years ago, this fluid is an original formulation, created as an alternative to potentially hazardous petroleum based fluids. The development of Rosco Fog Fluid was considered so innovative it won an Academy Award® for this technical achievement.
Created for use in the extensive array of Rosco fog machines, Rosco Fog Fluid can produce a wide range of effects - anything from a heavy cloud to light wisps. It is designed to provide maximum hang time.
Rosco Fog Fluid is available in 1-liter and 4-liter containers.


text:
Should Pediatricians Prescribe Kindness?
An innovative program shows that pediatricians may be able to contribute to healthy child development by encouraging kids to be kind.
Should Pediatricians Prescribe Kindness? | An innovative program shows that pediatricians may be able to contribute to healthy child development by encouraging kids to be kind.
When parents take their children to a pediatrician for a wellness check, they expect to get reports on their children's healthy development—if they're growing properly, eating and sleeping well, or in need of vaccines.
They probably don't expect to get a prescription for kindness.
But at Senders Pediatrics, a private practice in Cleveland, Ohio, and one of the Greater Good Science Center's 16 Parenting Initiative grantees, this is exactly what parents are getting. The clinic's parent education coordinator, Joan Morgenstern, has developed a program to produce events, lessons, and tools promoting kindness. Based on evidence that practicing kindness and purpose benefits children, the program helps kids care for others and flourish themselves.
While the program is in its infancy, it's a model that is popular with parents and kids, and has helped the staff at Senders Pediatrics—particularly during this difficult time of COVID. Shelly Senders, the clinic's founding pediatrician, hopes their focus on kindness and developing the "whole child" is a model that can be replicated more widely.
"My goal in all of this is to get the American Academy of Pediatrics to endorse the concept of teaching kindness in every pediatric practice," he says.
How and why to encourage kids to help others
There are many reasons to encourage kids to be kind. For one, it helps build positive relationships, which are important for developmental growth and success in life. More broadly, kindness is a moral virtue that can lead to more trusting, cooperative societies. And picking up kindness as a value from a young age can have positive effects later in life.
When kids are kind, they are happier and less likely to have social or behavioral problems. Kids who do nice things for others may have a greater sense of agency and purpose, too—meaning, they see that their actions can have a positive impact in the world and feel more capable of changing things for the better.
Given the benefits to mental health, it's not too surprising that kids who practice kindness could be physically healthier, too. At least one study found that adolescents randomly assigned to volunteer had significantly better cardiovascular function than those waiting to volunteer.
To launch Senders Pediatrics's kindness program, Morgenstern organized a Community Kindness Day in 2019 that gathered hundreds of families at a local community center. Researcher Stephen Post presented data showing that kindness improves physical and mental health, and booths were set up where kids could showcase their philanthropic work in the community and inspire other kids to get involved and find purpose by doing good.
"We really focused on our Cleveland community that had already taken initiative to get involved in community activism, social service, and serving others," she said. "The fact that we were having kids lead was probably one of the most impactful parts of the program."
According to Maurice Elias, a Rutgers University researcher who has been an advisor to Morgenstern and her program, the event was a success on multiple levels. It gave parents a reason to want to instill kindness in kids, legitimized the importance of social-emotional skills, and allowed kids to take charge.
"When kids engage in these kinds of public acts, it builds their skills, self-esteem, and confidence; it gives them an incentive to learn how to communicate better," he says.
The many ways Senders Pediatrics encourages kindness
Morgenstern comes from an education background, where the virtues of encouraging children's social and emotional growth have been understood for years. But to see this idea promoted in a pediatric clinic was novel for her.
"I thought, 'Wouldn't it be remarkable if doctors were spouting this message about social and emotional learning?' Because doctors exude a sense of authority (for some people, at least), that means people will pay more attention to the message," she says.
Her overall concept was to create opportunities for kids to acquire a habit of kindness that could be integrated into their lives, while also stressing the importance to health. After Kindness Day, she developed worksheets, activity cards, kits, monthly newsletters, and more—all aimed at promoting these important values while not stressing the busy pediatric staff.
Senders was particularly enthused about Morgenstern's kindness cards, which had ideas for practicing kindness at nearly any age and could be handed out at well checks.
"You can start at age three, four, or five, and integrate kindness into your regular well-child care visit," he says.
Unfortunately, some of Morgenstern's ideas had to be jettisoned when COVID hit, including the second annual Kindness Day. But she got creative and sent out card-making materials around Valentine's Day, encouraging kids to send valentines to people in the community who could use a boost—like first responders or elderly folks in nursing homes. In December, she asked kids to perform a good deed for someone else, providing kids with a "kindness kit" with more ideas of how to be kind.
According to Elias, these types of activities are successful because they expand a kid's idea of how kindness matters more broadly in the world and introduce kids to the intrinsic rewards of being kind.
"Kids begin to understand that many people need kindness," he says. "Once they start to think about these folks, and they do something kind and get a reply, that is incredibly reinforcing."
Morgenstern wasn't just interested in helping children, though. She also wanted to consider how parents were struggling during the pandemic. Many of them were having a hard time getting their kids to wear masks; so, Morgenstern began writing children's books, such as one called The Task of the Mask, which made salient to kids the reasons why mask-wearing was an act of kindness.
"Something like this is of tremendous value to parents, because it deals with the issues that parents are experiencing," says Elias. "It's something that's feasible that's not going to take much time but is engaging to kids."
Why Senders may be on to something
While it's unclear how much Senders's program can change the culture of a whole community, it has been well-received by parents whose kids have participated.
After attending Kindness Day, a seven-year-old patient at Senders Pediatrics, Garett, was encouraged to apply for a "B.E.E. Kind" grant the clinic designed, which paid for the creation of a "Kindness Corner" at his school. He and his mom, Shelly Hyland, purchased books on kindness, put Post-it notes in the school library where kids could leave kindness messages for each other, and created a snack cart for those who couldn't afford school snacks.
"I was able to teach my son about writing grants to get money and materials needed to support ideas and causes that he is passionate about," says Hyland. "He learned that he could make a difference with a simple idea even though he was only seven years old!"
Has it made a difference in her son's life? Hyland thinks so.
"I believe focusing on kindness has allowed my child to read others' emotions and have empathy," she says. "His idea and execution of the Kindness Corner drew support from the school and community, all of which had a positive impact on others."
Another parent, Chrishawndra Matthews, found that her son Derrick's interest in building literacy among boys in his community was encouraged by participating in Kindness Day, where he staffed a booth. Not only was his work honored, but he was also able to get his message across to others. "He was talking about the importance of reading and sharing with children how they can become stronger readers," says Matthews.
Matthews was particularly happy that Senders Pediatrics drew participants from the low-income neighborhood where she and her son live, recognizing that they are the experts in how to best help their community. And, she adds, it's important for kindness initiatives to empower people who may feel disenfranchised.
"I loved that, with the Kindness Day, it wasn't just about suburban folks; other communities were invited, and there was diversity," says Matthews, founder of Literacy in the Hood, which provides books for underserved communities.
These individual stories are not only heartening, but reflect how kindness can be contagious and good for all. Senders hopes that their program will be studied by researchers to validate what they are seeing anecdotally.
Matthew Lee, a Harvard researcher who coauthored a study on the physical health benefits of being a volunteer (in adults) and directs research at the Human Flourishing Program, is interested in doing just that. He appreciates the Senders Pediatrics approach to whole-child wellness.
"When a child goes to see the doctor, it shouldn't just be about taking your vital signs in a narrow, biophysical sense," he says. "Doctors should talk about how kindness relates to overall health, which includes physical well-being, sure, but also your mental well-being and your full flourishing."
Senders believes that their kindness initiative not only helps children develop moral character, it also makes them less afraid to go to the doctor—something that makes the staff's job easier. Before COVID hit, the clinic had a jungle gym set up in their waiting room available for kids to play on, which created a happy, welcoming environment for children. Now, they have a kindness program, which does much the same thing.
"It started out as something small, but it has become an integral part of how we operate in our office," says Senders. "It really has changed how we practice medicine."
This article by Jill Suttie, originally appeared on Greater Good Magazine, the online magazine of UC Berkeley's Greater Good Science Center. View the original article here.
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New Survey Reveals That Kindness is Doing Great!
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The Greater Good Science Center studies the psychology, sociology, and neuroscience of well-being, and teaches skills that foster a thriving, resilient, and compassionate society.
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The Jungle Book Review
So I watched The Jungle Book…
We live in a time where film is dominated by prequels, sequels and remakes. Now while many of these are often unnecessary and exist solely for big studios to make money, there are a portion of them that actually try to expand on beloved stories with new technology or fresh ideas. I was excited to see this movie because The Jungle Book is one of my favourite childhood films and the prospect of a live-action version was mind-blowing.
Okay, basic plot: After finding a baby boy alone in the jungle, Bagheera (Ben Kingsley) – a panther – takes the boy to a wolf pack and entrusts them with raising the boy who they name Mowgli (Neel Sethi). Mowgli struggles with jungle life but is happy to be part of the pack and the majority of the jungle has accepted his presence. This all changes when Shere Khan (Idris Elba) – a ferocious tiger – returns to the jungle and insists that Mowgli be killed because "man is forbidden". In an attempt to keep his family safe Mowgli leaves the pack to find a new home. In his journey he runs into Baloo (Bill Murray) – a fun-loving bear who takes Mowgli in. However, Mowgli's new home is far from safe as Shere Khan refuses to give up the hunt.
Remakes are extremely difficult to make. If you change too much, fans complain because the remake isn't enough like the original; f you don't change anything, fans complain and call you unoriginal. You're damned if you do and equally damned if you don't. I mention this because when you judge a movie like this, it's impossible to judge it solely on its individual merit. For many people this is going to be their first experience with The Jungle Book and they can judge it as a standalone movie but I've seen the original, I've loved the original so I need to compare. Now this might lead to some contradictory statements in my review but bear with me please.
First off, let's talk about the visual delight that this movie was. I didn't watch it in 3D but I still got the majestic aura that the groundbreaking visuals in this film provided. Nine times out of ten you can't tell that you're looking at something that was created on a computer screen. It's clear that the people behind this film's special effects worked incredibly hard on creating not only the look of a real jungle but also the feel. The way the animals move, their mannerisms; it was like watching an episode of National Geographic.
Now, while I thought the movie was an aesthetic achievement, I did find it lacking in terms of story. It's the classic jungle book story, beat for beat and while that worked great for a cartoon, I wanted something more for a live-action film. This film feels more like a conversion – with pieces of story and themes directly copied – instead of a transformation which displays growth and innovation. There were new elements in the story such as the laws that govern the jungle and the way that Mowgli had to compensate for his decreased strength and agility compared to his wolf peers.These elements add a fresh spin to the classic story but the film reverts to copying the original far too much. It's different but not different enough.
This film's voice cast is extremely impressive and the entire cast is phenomenal. Bill Murray as Baloo was the perfect casting choice. Murray has this great relaxed and worry-free attitude which is perfect for the character. Christopher Walken as King Louie (one of my favourite characters from the original) was also fantastic. I had a slight problem with Ben Kingsley and Idris Elba. Both men do wonderful jobs voicing their characters – Bagheera and Shere Khan, respectively -but I felt they would have been better if they had switched roles. This is just a matter of personal preference and isn't really a 'problem' per se.
Neel Sethi – who stars as Mowgli – certainly had a difficult role to fill in this. He's the only 'real' character on screen and needed to be this film's heart and anchor. Earlier I said that the film's visuals work nine times out of ten; in the same way, Sethi's acting works nine times out of ten. It's that one time that bothered me. There are moments when you can tell that Sethi is just staring at a green screen and these moments remind you that you're just staring at computer generated images and this removes the wonder of the whole affair. Don't get me wrong, this kid does a great job and he'll probably be one of your favourite things about the movie.
I previously stated that remakes are damned if they do and equally damned if they don't. I've complained about the story and how it wasn't different enough and now I'm going to complain about the songs and how they weren't familiar enough. The original Jungle Book was a disney cartoon and, as such, had big, beautiful musical numbers with choreographed dancing and insanely catchy beats. This movie still has the songs but they're performed in a much more simplistic and improvised manner. So instead of music swelling up and people being moved to song; characters speak and then kind of gradually drift into singing. I didn't really like it because I wanted those elaborate musical performances but I recognise why director, Jon Favreau chose to present them this way. This movie wishes to ground itself in reality and random music and songs wouldn't really fit in.
Overall, The Jungle Book isn't perfect but I think it does well to stand up to the original and establish itself as an independent piece of work. There are things that are different and others that aren't so if you've watched the original, prepare to be disappointed but also pleasantly surprised. See, everything I say is a contradiction. Long story short, this film is worth watching and will introduce a classic story to a whole new generation. 7/10
Posted in Movie reviewTagged bill murray, disney, idris elba, jon favreau, lupita nyongo'o, Movie review, the jungle bookBy Kgothatjo Magolego3 Comments
3 thoughts on "The Jungle Book Review"
Robert Lambert Jones III says:
This will be a rental for me. Generally, I like Favreau's work. Have you seen Chef? If not, I recommend it.
Khalid Rafi says:
I dunno. I just couldnt get into it. The live action animals ironically made the film feel dull and lifelss for me and in the end I was all too bored.
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Reconciliation Saskatoon is a is a community of 57 organizations, non-profits, businesses and partners who have come together towards one mission: to initiate a citywide conversation about reconciliation and provide opportunities for everyone to engage in Calls to Action.
They invite others to attend and participate in Reconciliation activities leading up to and including National Aboriginal Day and the "Rock Your Roots" Walk for Reconciliation as a means of learning about reconciliation and expanding the network of people committing to their own Call to Action.
These activities offer Saskatoon residents the opportunity to learn, in a safe environment, about the residential schools, their ongoing legacy today, and also the incredible resilience and strength of Saskatchewan's Indigenous community.


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Home » Arcadis Mum on Decision to Counter Japanese Offer For UK Consultant Hyder
Arcadis Mum on Decision to Counter Japanese Offer For UK Consultant Hyder
Peter Reina
Netherlands-based ARCADIS remains mum on a decision to counter a higher offer from Japan's Nippon Koei to acquire Hyder Consulting plc., London.
The Tokyo-based firm appears to have outbid its Dutch rival with an Aug. 8 offer that is 4.6% more than the ARCADIS bid and that Hyder recommended favorably to shareholders.
That bid came eight days after Hyder had accepted Arcadis's $430-million cash bid for all its shares. An ARCADIS spokesman told ENR on Aug. 11 there was no change in its Aug. 8 statement that the firm is "considering its position" and would announce its next step "in due course."
Tokyo-based Nippon Koei Group ranks at No. 67 on ENR's list of The Top 150 Global Design Firms, with $514.8 million in 2013 worldwide engineering revenue, about $157.1 outside of Japan.
Arcadis ranks at No. 10 on that list, with $3.3 billion in global design revenue; Hyder ranks at No. 70.
The U.K. firm, with 52% of its $472 million in transportation, would boost capabilities for the Japanese bidder, which reported 27% of revenue in that sector.
"The Japanese have been trying to reenter the global E&C market in a bigger way after years of standing aside to the Koreans, and more recently to the Chinese," says one industry executive. "Japanese firms have been in the hunt for an acquisition for the last several years but without any notable successes in my view."
Meanwhile, British contractor Balfour Beatty rejected on Aug. 11 an overture from a smaller UK builder, Carillion, to renew acquisition talks.
The talks had broken down orginally late last month over Carillion's insistance that Balfour Beatty cancel its planned divestiture of New York City-based consultant Parsons Brinckerhoff, which it has owned since 2009.
Balfour Beatty said Carillion's new proposal still did not resolve the larger firm's concerns that continuing talks could deter the Parsons Brinckerhoff deal and hurt the U.S. firm's value
Sweetened Offer Seals ARCADIS Buy of UK Engineer Hyder for $476 Million
Holland's Arcadis Set To Buy U.K. Consultant EC Harris for up to $200 Million
Materials for Sustainable Sites: A Complete Guide to the Evaluation, Selection, and Use of Sustainable Construction Materials