Differences in levels of E. coli contamination of point of use drinking water in Bangladesh.

This study aimed to quantify the inequalities and identify the associated factors of the UN sustainable development goal (SDG) targets in relation to safe drinking water. The concentration of the gut bacterium Escherichia coli in drinking water at the point of use (POU) and other information were extracted from the latest wave of the nationally representative Bangladesh Multiple Indicator Cluster Survey (MICS 2019). Bivariate and multivariable multinomial logistic regression models were used to identify potential predictors of contamination, whereas, classification trees were used to determine specific combinations of background characteristics with significantly higher rates of contamination. A higher risk of contamination from drinking water was observed for households categorized as middle or low wealth who collected water from sources with higher concentrations of E. coli. Treatment of drinking water significantly reduced the risk of higher levels of contamination, whereas owning a pet was significantly associated with recontamination. Regional differences in the concentrations of E. coli present in drinking water were also observed. Interventions in relation to water sources should emphasize reducing the level of E. coli contamination. Our results may help in developing effective policies for reducing diarrheal diseases by reducing water contamination risks.

Introduction

Higher mortality rates from diarrheal diseases, predominantly in low- and middle-income countries, can be substantially reduced through interventions such as the provision of safe drinking water [1]. The availability of the latter for consumption is a human right [2], as reflected in the United Nations Sustainable Development Goals (SDGs) as safely managed targets [3-5] targets. Previous studies have shown that drinking water at the point of use (POU) is more likely to be contaminated than water collected from main sources, and, hence, the efforts required to achieve the SDG targets beyond the source infrastructure are more extensive and challenging [6-9]. In addition to the chemical and physical aspects, the quality of drinking water in relation to health issues may be assessed from the presence and concentration of microbial contamination [10, 11].

The SDG target of “safely managed” water, in terms of being free of microbial contamination, can be assessed from the presence and concentration of the human gut bacterium Escherichia coli in drinking water. Based on the level of contamination, drinking water at source or at the POU is categorized with respect to potential health risks [12, 13]. To monitor the achievements of safely managed drinking water in relation to the SDG targets and develop related policy recommendations, recent literature was searched with the aim of determining the factors associated with E. coli concentration in drinking water. One of the major sources of contamination is the collection point where drinking water may have been exposed to microbes from the environment. Water collected from an “unimproved” source is more likely to be contaminated during extraction, which may become incorporated in POU drinking water [12, 14–17].

The type of main drinking water source and the level of contamination at the collection point have associations with the level of contamination at the POU. However, the association between improved water sources and the level of fecal contamination may not be decisive [18, 19]. Higher levels of fecal contamination in household water are associated with unimproved sanitation facilities [20]. If households use open defecation or unimproved facilities, the water source can be exposed to microbes from the excreta, and consequently, the quality of drinking water deteriorates. Recontamination of drinking water occurs as a result of poor management of household water resources [21, 22]. Increased risk of higher levels of contamination at the point of consumption is significantly associated with ownership of livestock [23].

A clear understanding of the health impacts of maintaining the quality of potable water can be achieved by passing on information to individuals through educational institutions and/or community initiatives. A positive association between hygienic water practice and the level of educational attainment has been reported in the recent literature [24]. For example, a study based on data from Ghana, Nepal and Bangladesh showed that ownership of any type of livestock was associated with an increased risk of fecal contamination of drinking water at the POU [25, 26]. Adequate water treatment methods at the POU can significantly reduce the presence and total counts of coliforms present in drinking water [27-29]. Socio-economic inequalities among households are usually reflected in access to quality livelihoods, especially in terms of accessing potable water. A higher asset index score measured in terms of household possessions was significantly associated with access to improved water sources and reduced E. coli contamination in the drinking water [30, 31]. In the literature, the general practice is to convert the concentration of E. coli in drinking water into categories of potential health risks. The standard procedure of measuring the association of categorical outcome variables with a set of covariates is to measure the adjusted odds ratio (AOR) in a multivariable analysis [12, 32–34].

There is a gap in the existing literature with regard to identifying the specific combinations of background characteristics associated with higher risks of E. coli contamination in POU drinking water in Bangladeshi households. To fill this gap, this study aimed at understanding the distribution of and differences among the level of E. coli contamination in different areas of Bangladesh. To accomplish the aim, unadjusted and adjusted associations between E. coli contamination and a set of covariates were estimated using bivariate and multivariable analyses. A machine-learning tool, the classification tree, was used to identify the distribution of E. coli concentration over interactions of the predictor variables. The analyses were conducted using the latest data of the nationally representative Multiple Indicator Cluster Survey (MICS) of Bangladesh conducted in 2019. Our results have implications for a better understanding of the SDG target 6.1 and for providing empirical evidence to support the development of feasible and effective plans to reach this target.

Material and methods

Data

This study was conducted using data from the latest wave of the Multiple Indicator Cluster Survey conducted in Bangladesh in 2019 (MICS 2019) [35]. The survey was designed to achieve reliable estimates at the national level different across urban-rural areas, administrative divisions and districts of Bangladesh. A two-stage, stratified cluster sampling technique was adopted for the purpose of survey implementation. The first stage sampling frame consisted of the primary sampling units (PSUs) obtained as the enumeration areas (EAs) based on the latest Bangladesh Population and Housing Census-2011. The main strata were defined as the urban and rural locations within each of the 64 districts. A probability proportional to size (PPS) sampling procedure was used to select the PSUs (3220) from each of the sampling strata. For each of the selected EAs, complete lists of households were prepared for the next stage of sampling. A systematic random sample of 20 households was drawn from each of the 3220 EAs selected in the first stage. From these selected households from each EA, four households were selected for assessing arsenic concentration in their drinking water. From the four selected households from each of the EAs, two households were randomly sampled for assessing E. coli concentration in household drinking water and at its “source”. The sampling in these two stages was done using a systematic random sampling technique. Thus, the expected sample size of this study was 6440 households, which were selected for testing of E. coli. A total of 6069 (98.7%) of the selected households were successfully tested for both household and source water quality for E. coli concentration. A total of 17 cases, for which results were either lost or unreadable, were excluded from the study.

Variables

Outcome variable

The dependent variable in this study was the quality of drinking water in terms of possible fecal contamination. The water samples were collected from each household by asking for “a glass of water that you would give a child to drink.” The most recommended indicator for fecal contamination of drinking water is the number of bacteria counts of E. coli in a 100 ml sample of drinking water. For this purpose, 100 ml of sample water was filtered through a 0.45 μm filter (Millipore Microfil®, MilliporeSigma, Burlington, MA, USA) and placed onto a Compact Dry EC growth media plate (R-Biopharm AG, Darmstadt, Germany). After 24 hours of incubation at ambient temperature, the number of blue colonies was recorded. Household drinking water with less than one blue colony was termed “low risk”, whereas those with 1–10 colonies were categorized as”medium risk”, and samples with 11–100 colonies or more than 100 colonies were categorized as “high” or “very high” risk, respectively [36]. In this study, the last two categories were combined into a “high risk category”.

Predictor variables

In our study, a set of predictor variables were used to test a possible association with the outcome variable. The choice of predictor variables was guided by the existing literature, knowledge of the researchers and the availability of information. E. coli contamination in the drinking water may have occurred at the point of water collection. The information about this variable was recorded and categorized in the same way as the outcome variable. The types of drinking water sources (categorized as “improved” and “unimproved”) were considered as potential predictor variables. The locations of the drinking water sources may have been linked to water quality in two ways, as they were located in areas surrounded by cleaner environments, or they may have been contaminated through the process of carrying the drinking water back to the households. Based on the locations of the drinking water sources, households were categorized as those having the sources in dwelling/ premises or elsewhere. The other predictor variables included in the analysis were: whether the water was treated or untreated, the type of toilet facility (improved and unimproved), place of residence and administrative division [37, 38]. Our study tested the hypothesis that the educational attainment of the head of the household influences the behavior of the household members when consuming safe drinking water. Based on their educational attainment, heads of households were categorized as either “no education” or “pre-primary”, “primary” or “secondary level” of education or “higher level” of education. Several studies observed a positive association between ownership of livestock and contamination of water. According to the ownership status of any livestock, herds, other farm animals, or poultry, households were categorized as either “own” or “do not own” any of livestock. Our study also tested whether wealthy households, with better management, were able to keep contamination to a lower level. Based on whether households had greater ability to manage safe drinking water, the variable was categorized as either “a poor,” “middle” or “rich” households.

Statistical analysis

In determining the unadjusted association between E. coli contamination and selected background characteristics, a bivariate analysis was conducted. As the outcome variable and all covariates were categorical by nature, bivariate chi-square analyses were also carried out. In addition to the bivariate analysis, a multivariate analysis was applied in order to determine the adjusted association between the covariates and E. coli contamination in household drinking water. The outcome variable, E. coli contamination in household drinking water, had three levels, coded as 2 (for high concentration), 1 (for moderate concentration) and 0 (for low concentration). For a multivariable analysis with three levels of outcome variables, a multinomial logistic regression was employed and details of the model can be accessed in the existing literature [39-41]. This model has numerous applications in the domain of population health [42, 43].

In order to identify significant multifactor interactions of the covariates associated with the level of E. coli contamination, a classification tree was used. The methodology was guided by the conditional inference framework [44, 45]. Squared adjusted generalized variance inflation factor (GVIF) scores were used to quantify multicollinearity in the model [46]. Version 3.5.3 of the open-source software R [47] package and version 1.2–7 of the related partykit package [48] were utilized in order to analyze the data and to fit the models.

Results

The results of the bivariate analysis showing the relationships between E. coli contamination in drinking water and the potential covariates are presented in Table 1. A higher level of contamination in the source of the drinking water resulted in a higher level of contamination at the POU, and this association was statistically significant (p<0.001). the type of the source of drinking water and the location of the facility were significantly associated with the level of E. coli contamination at the POU. E. coli contamination were significantly higher in water from unimproved sources than from improved sources. The contamination was significantly lower in water collected from sources located in households or premises than from those located outside. The results of the bivariate analysis also indicated that the proportion of households with E. coli contamination was lower for those using any water treatment methods. The percentages of households with higher levels of E. coli contamination were significantly lower in households with improved toilet facilities. Ownership of livestock significantly increased the likelihood of consuming drinking water with possible fecal contamination. Higher levels of education of the head of household resulted in lower levels of E. coli contamination in drinking water. Among the households categorized as poor, middle or rich, the proportion of sample households with low levels of E. coli contamination were 15.0%, 17.4% and 21.4% respectively. A higher percentage of households in rural locations (62.8%) consumed water with high E. coli concentrations than those in urban locations (56.8%). The percentage of households with high E. coli concentrations in drinking water was highest in Barisal Division, followed by Dhaka Division. The percentage was lowest in Rangpur Division, followed by Mymenshing Division. The regional differences in the proportions of households with high levels of E. coli contamination were statistically significant.

Table 1

Percentage distribution of households with various level of E. coli contaminations in drinking water at the point of use (POU).

	E. coli in POU Drinking Water			Sample Size (N)
	Low	Moderate	High
E. coli in water source (p < 0.001)
Low	25.9	23.3	50.8	3741
Moderate	6.6	21.3	72.0	1326
High	4.4	6.8	88.8	985
Education of household head (p < 0.001)
No or pre primary	15.5	19.6	65.0	2142
Primary	17.4	19.4	63.1	1714
Secondary or higher	21.4	21.4	57.2	2196
Ownership of livestock (p < 0.001)
No	21.4	20.6	58.0	2354
Yes	16.1	19.9	64.0	3698
Household wealth status (p < 0.001)
Poor	15.0	19.7	65.3	2796
Middle	18.8	20.1	61.1	2350
Rich	26.3	21.9	51.9	906
Source of drinking water (p < 0.001)
Improved	18.6	20.3	61.1	5877
Unimproved	4.0	15.4	80.6	175
Location of drinking water source (p < 0.001)
Dwelling/ Premises	19.3	20.7	60.0	4373
Elsewhere	12.1	19.2	68.7	1260
Treatment of drinking water (p = 0.002)
No	17.6	20.2	62.1	5558
Yes	24.3	19.4	56.3	494
Type of toilet facility (p < 0.001)
Improved	19.0	20.5	60.5	5027
Unimproved	14.3	18.5	67.1	1025
Place of residence (p < 0.001)
Rural	17.1	20.1	62.8	4896
Urban	22.8	20.3	56.8	1156
Administrative division (p < 0.001)
Barisal	9.3	21.5	69.2	558
Chittagong	18.3	22.3	59.5	1046
Dhaka	16.9	16.6	66.6	1232
Khulna	15.3	19.9	64.8	947
Mymenshing	21.9	24.3	53.8	370
Rajshahi	21.9	15.4	62.7	764
Rangpur	24.5	24.8	50.7	747
Sylhet	18.8	21.4	59.8	388

A multivariable analysis using a multinomial logistic regression was employed to quantify the adjusted impacts of the covariates on E. coli contamination of drinking water with three levels. All of the significant variables in the bivariate analysis were included in the model. Because of possible multi-collinearity of the source and location of the drinking water, the source location of the latter was excluded from the model. The model outputs, along with the AOR and 95% confidence intervals (CI), are presented in Table 2.

Table 2

Adjusted odds ratio (AOR), confidence intervals (CI) and P-values of moderate and high risk of E. coli contamination of drinking water at the point of use (POU) obtained from the multinomial logistic regression models.

	Moderate risk		High risk
	AOR (LCL–UCL)	P-Value	AOR (LCL–UCL)	P-Value
(Intercept)	0.44 (0.29–0.67)	0.000	0.37 (0.25–0.53)	0.000
E. coli in water source
Low	1.00	---	1.00	---
Moderate	3.79 (2.92–4.92)	0.000	6.32 (4.98–8.02)	0.000
High	1.80 (1.19–2.71)	0.005	12.92 (9.28–17.98)	0.000
Ownership of livestock
No	1.00	---	1.00	---
Yes	1.17 (0.97–1.40)	0.102	1.35 (1.15–1.58)	0.000
Source of drinking water
Improved	1.00	---	1.00	---
Unimproved	2.51 (1.04–6.03)	0.043	1.70 (0.75–3.85)	0.206
Treatment of drinking water
Yes	1.00	---	1.00	---
No	1.44 (1.04–1.99)	0.026	1.80 (1.35–2.40)	0.000
Education of household head
Secondary or above
No/Pre primary	1.10 (0.89–1.37)	0.370	1.18(0.98–1.42)	0.083
Primary	0.98 (0.79–1.22)	0.887	1.09(0.90–1.31)	0.377
Type of toilet facility
Improved	1.00	---	1.00	---
Unimproved	1.01 (0.79–1.30)	0.865	1.14 (0.92–1.42)	0.214
Household wealth status
Rich	1.00	---	1.00	---
Middle	1.14 (0.87–1.49)	0.335	1.43 (1.13–1.81)	0.003
Poor	1.28 (0.95–1.73)	0.106	1.74 (1.33–2.26)	0.000
Place of residence
Rural	1.00	---	1.00	---
Urban	0.91 (0.72–1.15)	0.429	0.85 (0.70–1.04)	0.124
Administrative division
Rangpur	1.00	---	1.00	---
Barisal	2.55 (1.73–3.77)	0.000	4.41 (3.10–6.26)	0.000
Chittagong	1.16 (0.86–1.56)	0.310	1.21 (0.93–1.59)	0.154
Dhaka	1.03 (0.76–1.38)	0.820	1.82 (1.41–2.40)	0.000
Khulna	1.24 (0.91–1.69)	0.164	2.06 (1.57–2.70)	0.000
Mymenshing	0.97 (0.67–1.41)	0.941	0.87 (0.62–1.22)	0.418
Rajshahi	0.75 (0.54–1.03)	0.073	1.54 (1.18–2.00)	0.001
Sylhet	1.25 (0.84–1.84)	0.255	1.59 (1.13–2.25)	0.008

In the multinomial logistic regression model, a higher concentration of E. coli in the source of drinking water was significantly associated with a higher concentration E. coli in the POU drinking water. For example, households collecting water from a high-risk contaminated source were 11.92 (AOR: 12.92; 95% CI: 9.28–17.98) times more likely to have a high risk of E. coli contamination at the POU. On the other hand, for sources providing water with a moderate risk of contamination, the POU water was 5.32 (AOR: 6.32; 95% CI: 4.98–8.02) times more likely to have a high risk of E. coli contamination at the POU. Households collecting drinking water from unimproved sources were more likely to consume water with a higher level of E. coli contamination than those collecting from improved sources. The relationship was statistically significant for a moderate risk of contamination (AOR: 2.51, 95% CI: 1.04–6.03).

Households not treating their drinking water after collection were significantly more likely to consume water with a moderate (p < 0.05, AOR: 1.44; 95% CI: 1.04–1.99) or high risk (p < 0.001, AOR: 1.80; 95% CI: 1.35–2.40) of E. coli contamination. Ownership of a pet was significantly associated (p<0.001) with the consumption of water with a higher level of E. coli contamination. Compared with households not owning livestock, those owning livestock were 0.35 (AOR: 1.35; 95% CI: 1.15–1.58) times more likely to consume drinking water label as having a high risk of contamination. The other variable related to the household environment was the type of toilet facility available in the household. Not possessing a toilet facility or using an unimproved one was related to a higher risk of E. coli contamination of drinking water.

Wealth of household showed a consistent association with the level of E. coli contamination of potable water, though the relationship was more evident when considering the contamination in relation to higher risk. For example, with respect to rich households, middle and poor households were 0.43 (AOR: 1.43; 95% CI: 1.13–1.81) and 0.74 times (AOR: 1.74; 95% CI: 1.33–2.26) times more likely, respectively, to consume water with a high level of E. coli contamination. Urban households were less likely to consume E. coli contaminated drinking water at the POU, though the differences were not statistically significant. This study identified significant regional inequalities in E. coli contaminated water at the POU of household members. Households residing in Barisal and Khulna divisions were 3.41 (AOR: 4.41; 95% CI: 3.10–6.26) and 1.06 (AOR: 2.06; 95% CI: 1.57–2.70) times more likely, respectively, to use high E. coli contaminated drinking water compared to those residing in Rangpur Division.

The levels of contamination risks related to E. coli concentration in drinking water at the POU in Bangladesh for various combinations of the levels of background of households is presented in Fig 1. The root node of the classification tree is the level of E. coli contamination in the source of the water. This finding indicates that contamination of water at source was the major contributor to the risk of contamination in POU drinking water. Urban-rural residence did not appear to be a significant contributor to the outcome variable when the households collected drinking water with high level of E. coli contamination. The immediate left of the trunk is divided into branches based on the wealth of the households. For households consuming water from highly contaminated sources, the contamination level of the POU drinking water did not differ significantly between households with poor or middle incomes. For this group, the highest percentages of households (91.0%) had a high level of E. coli contamination in the POU drinking water (node 6). The third node (second node to the left trunk) indicates significant regional differences in the level of E. coli contamination (among the households categorized as rich and using drinking water from sources with high levels of E. coli contamination). The right trunk from the root node refers to households collecting drinking water with low or medium levels of E. coli contamination. All of the terminal nodes through this trunk (nodes 9, 11, 14, 15, 16, 18, 20 and 21) were related to relatively lower percentages of households with high level of E. coli contamination. From this group, a relatively higher proportion of households (67.0%) from Barishal Division had higher levels of E. coli contamination in their drinking water (node 9). These proportion was followed (66.4%; node 15) by the poor households in Dhaka and Khulna Divisions and the middle/poor households from Rajshahi Division (62.1%; node 16).

Fig 1

Classification tree representing the distribution of levels of E. coli contamination of drinking water across different combinations of the levels of household characteristics.

Discussion

This research quantifies the inequalities and identifies the associated factors of the SDG targets relating to the safe POU drinking water in Bangladesh based on microbiological quality. The results from of this study confirmed that the source of the drinking water did not have any significant association with the level of E. coli contamination in POU water and that using any water treatment method significantly reduced the concentration of E. coli concentration in POU drinking water.

This study also identified that a higher concentration of E. coli in the water source was significantly associated with the concentration of E. coli in the POU drinking water. This fact was supported by a number of studies conducted in Bangladesh and elsewhere [22, 49–52]. The positive association between the proportion of households with high concentrations of E. coli in POU water and also owning a pet might indicate the possibility of secondary contamination. Middle/poor households with high level of E. coli contamination at source had an approximately 90% risk of having a high level of contamination in their POU water.

The results of this study suggest that a higher proportion of E. coli contamination originated from the source of the drinking water. Emphasis should be put on improving the infrastructure of water sources in order to ensure that water is not being contaminated through the surrounding environment. Integrated plans need to be formulated by the central and local government and nongovernmental organizations (NGOs) in order to provide affordable and adequate water treatment facilities to the mass population. Significant regional differences in microbial contamination levels demands the adoption of alternative approaches at regional and local levels.

Conclusions

Using nationally representative data and sophisticated statistical tools, this study identified associated factors of E. coli contamination in point of use (POU) drinking water in Bangladesh.

E. coli contamination of POU drinking water was significantly associated with the contamination of water at its source but not with the source type (improved or unimproved). Hence, the key factors of contamination at the source of collection should be identified and measures should be taken to avoid such contamination. Rural household should be educated regarding the possible recontamination of drinking water by livestock. Use of household water treatment facilities significantly reduces the E. coli contamination of drinking water, though the use of such facilities is limited. Integrated campaigns to promote the importance of treating water before drinking may raise the current rates of water treatment by households. Potential water treatment users should also be educated in how to use the methods more effectively in order to make water safe for drinking, and the materials for water treatment should be readily available. Significant regional differences in the levels of E. coli contamination in POU drinking water should be kept in mind in developing relevant policies. One of the limitations of our study was that the cross-sectional data were collected at a particular point in time, and so the seasonal effects on water quality were not assessed. Information regarding the distances of water sources from toilet facilities was also not available.

23 Dec 2021

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Reviewer #1: I think the topic is relevant and can broaden our knowledge on the situation related to SDG 6 in Asia. But more works to do by authors.

Major comments

- Please check the writing. The sentences are not well written as an academic journal. I strongly suggest authors to do a professional proofread before submit the revision.

- There are many repetition as well in the text.

- The story line of the paper is bad. Authors need to revise the whole draft.

- The way the authors write E. coli is not correct. Please revise the whole paper.

- There are many sentences that need references.

- After reading the paper, I guess the authors are not really familiar with the drinking water quality. E.g., writing E. coli is wrong, some terms are rarely used in the WASH sector (e.g., secondary contamination, source water). I suggest authors to read some papers related to WASH and water quality and use common terms in this field.

- Authors should add references from region outside Bangladesh, not only from Bangladesh. So the findings are relevant also to other countries/settings.

Please check specific comments in the pdf file. I put my comments there. Thank you and good luck for the authors!

Reviewer #2: General:

The manuscript is technically sound. Though it has used secondary data, the data was analyzed vigorously with an appropriate use of statistics. Data supports conclusions that have been drawn by the author. The study incorporated an appropriate number of samples, and the methodology used for statistical analysis is latest and appropriate for the study.

Major:

One of the aims of the paper is to monitor the empirical evidence to monitor the progress of the sustainable development targets in relation to the safe drinking water usage in Bangladesh. This aim was not achieved, or it is not clear how author intended to achieve this aim.

In introduction, it is not mentioned, why this study is new and different from the existing studies. Identifying the research gap is not clear. Suggest authors to enumerate more on literature to highlight the gaps in the current research studies and the novelty of this work.

In results (statistical analysis), Adjusted Odds Ratio (AOR) interpretation was not done correctly. It is not clear on what basis; the author subtracts 1 from AOR values. Authors need to use the AOR values or explain with clear base why the subtraction is needed.

In results, when discussing regional changes of E. Coli values, “Mymenshing” division values must be used as it is the only lower value than the “Rangpur” division value which is the default. Or explain that it has been disregarded as the values are not significant.

The conclusion is not addressed to the aims of the study fully.

Limitations of the study, recommendations to address for future works are not included which is also a major limitation of the paper.

Minor:

Abstract:

*2nd para, 1,2,3 lines. Using of “if” is not appropriately done which alter the idea of the sentence.

Introduction:

* 2nd para, 2nd line. "Or other" written in brackets, it's not clear which other microbes the author tries to specify. It is better to name a microbe or maybe a family of microbes.

* 2nd para, 12th line. Better to start "Type of ma...." as a new paragraph as it discusses a new point.

* 2nd para, 14th line. "improved sources may have faecal contamination above the WHO standard" here above, does not give the idea that improved sources have minimum vulnerability to contaminate.

* 2nd para, 20th line. Better to start "A clear understanding ..." as a new paragraph as it discusses a new point.

* 2nd para, 31st line. It is unclear who "respondents" refers to.

Variables: Predictor variables

* 1st para, 4th line. "may be carried through the source of water collection", should be corrected as "may be occurred at the source of water collection"

Statistical Analysis:

* 1st para, 14th line. 35,36 references should be superscript.

Results:

* 1st para, 2nd line. reference not available

**********

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: PONE-D-21-23710_review.pdf

Click here for additional data file.

1 Mar 2022

Reviewers' comments to the author:

Reviewer #1:

Overall comment: I think the topic is relevant and can broaden our knowledge on the situation related to SDG 6 in Asia. But more works to do by authors.

Response: The authors appreciate positive comments from the reviewer. The manuscript has been revised following the valuable comments and suggestions of the reviewer.

Major comments

Comment: Please check the writing. The sentences are not well written as an academic journal. I strongly suggest authors to do a professional proofread before submit the revision.

Response: The manuscript has been proofread by a professional native English proofreader

Comment: There are many repetition as well in the text.

Response: the repetitions have been removed.

Comment: The story line of the paper is bad. Authors need to revise the whole draft.

Response: To improve the storyline, the whole manuscript has been revised carefully.

Comment: The way the authors write E. coli is not correct. Please revise the whole paper.

Response: The whole paper has been revised to replace E-Coli by E. coli.

Comment: There are many sentences that need references.

Response: References have been added as appropriate.

Comment: After reading the paper, I guess the authors are not really familiar with the drinking water quality. E.g., writing E. coli is wrong, some terms are rarely used in the WASH sector (e.g., secondary contamination, source water). I suggest authors to read some papers related to WASH and water quality and use common terms in this field.

Response: Revised.

Comment: Authors should add references from region outside Bangladesh, not only from Bangladesh. So the findings are relevant also to other countries/settings.

Response: Added

Please check specific comments in the pdf file. I put my comments there. Thank you and good luck for the authors!

Abstract:

Revision: Just write this MICS 2019

Response: Revised.

Revision: I guess only the classification tree was used to find the combinations, not the regressions

Response: Revised.

Revision: What do you mean by secondary contamination?

Response: Revised.

Revision: Do you have a hypothesis that inequalities exist? I suggest to change word evident here.

Response: Revised.

Revision: water source not source water

Response: Revised.

Revision: The last sentence doesn't add something. it applies to all places in the world

Response: Revised.

Introduction:

Revision: in 2017, 1.6 million deaths globally including half a million children -> dont put it in the brackets, just write it in the text.

Response: Revised

Revision: just focus on SDGs and not MDGs

Response: Revised.

Revision: previous, not contemporary

Response: Revised.

Revision: the SDG targets beyond the infrastructure of source is more practical and challenging -> I dont understand why you write it more practical.

Response: Revised.

Revision: health issue is not only about microbial, there are other chemical and physical aspects of water that also related to health

Response: Revised.

Revision: this is not the right way to write E. coli

Response: Revised.

Revision: unhygienic environment? this is the first time I heard this term. suggest to change this.

Response: Revised.

Revision: improved is only in MDGs. suggest dont use it here again.

https://washdata.org/monitoring/drinking-water

Response: Revised.

Revision: I never heard secondary contamination. suggest to change it to recontamination.

Response: Revised.

Revision: suggest delete the words from "such as...".

Response: Revised.

Revision: I am wondering why you have education/promotion here. this paragraph is too much information. the story line is also poor. you in the next sentence, your have livestock. why don't combine it with the sentences about secondary contamination above?

I mean you can discuss first sources of contamination and then discuss WASH education/promotion/socio-economic determinants. now this paragraph looks messy.

Response: Revised according to the suggestion.

Revision: This paragraph should be in the methods, not introduction!

Response: Revised.

Revision: Do you use this bayesian? if not, why you mention it?

Response: Revised.

Revision: What is the knowlegde gaps?

Response: Revised.

Revision: repetition of the methods in the previous paragraph.

Response: Revised.

Material and Methods

Data

Revision: I guess all the procedure here is obtained from the MICS report? if yes, you must cite the source. Unless you did this study yourself.

Response: Cited

Variables

Outcome variable

Revision: This is not only in Bangladesh. WHO recommends in 100 ml sample in all over the world.

Response: Revised.

Revision: Again, I criticize a lot the storyline. why you have the sentence about the blue colonies here? it should appears after the sentence about incubation.

Response: Revised.

Revision: Please give references to this.

You can use this one: https://doi.org/10.3390/resources9120150

Response: Reference added

Predictor variables

Revision: Please put the references. maybe you can move the paragraph about factors associated with water quality in introduction to this section.

Response: Revised and reference added.

Revision: why you need to put administratif area as predictor? check this paper why they use region as predictor:

Understanding handpump sustainability: Determinants of rural water source functionality in the Greater Afram Plains region of Ghana

Response: Revised.

Revision: education has a significant relationship with water quality has been found in many studies. please find the citation.

Response: Revised.

Statistical Analysis

Revision: bivariate effects? never heard this term

Response: Revised.

Revision: dont write it X2 (this is the test symbol), but chi-square (name of the test).

Response: Revised.

Revision: so what is the benefit of using bivariate tests?

Response: Revised.

Revision: multivariable approach? never heard this term. multivariate analysis

Response: Revised.

Revision: what variable? outcome?

Response: Revised.

Revision: three levels -> plural this sentence is not grammatically correct.

Response: Revised.

Revision: suggest to change trichotomous with multinominal logistic regression. Never heard trichotomous before.

Response: Revised.

Revision: never heard most stable models -> is it stable?

Response: Revised.

Revision: so others methods are not meaningful? the word choice it not good.

Response: Revised.

Revision: suggest to remove this equation, unless you did economic study.

Response: Removed.

Results

Revision: what is this?

Response: Revised.

Revision: again story line!! put results about education together with other socio-economic variables (next paragraph).

Response: Revised.

Revision: if not significant, then dont need to mention it

Response: Revised.

Revision: why you use Rangpur as the reference? please explain it in the methods

Response: Based on the prevalence.

Discussion

Revision: I think this study does not monitor the progress, because you use data which already presented in the MICS report.

Response: Revised.

Revision: I think you can remove this sentence. repetition of methods.

Response: Removed.

Revision: Please check my comments before. why then you use bivariate?

Response: Revised.

Revision: This first paragraph is repetition of introduction and methods! delete!

Response: Removed.

Revision: what do you mean by negative association? if they do HWT, the quality is poor? This is what I get from your sentence/writing.

Response: Removed.

Revision: which findings? HWT and E. coli concentration? why you jump directly to diarrhea while you discuss HWT before?

Response: Revised.

Revision: not only in Bangladesh. in many studies as well:

https://doi.org/10.2166/ws.2011.064

https://doi.org/10.1080/09603120410001725612

https://doi.org/10.3390/ijerph17072172

Response: References added.

Revision: we dont use improved water source in SDGs. so you cannot suggest this anymore.

https://washdata.org/monitoring/drinking-water

Response: Revised.

Revision: how about the cost? Or my question is: is your recommendation possible, considering the cost?

Response: Revised.

Revision: you have too much recommendations! Please just give one (the most possible) recommendation, based on your findings. Not throw everything here.

You also need to back up with references!

Response: Revised.

Revision: this last sentence is kind of empty. I mean of course you need to improve the quality to prevent diseases

Response: Revised.

Revision: Does you study has limitation or recommendation for future research?

Response: Revised.

Conclusion

Revision: major? It is a problem, but maybe not a major issue in of public health in Bangladesh. I mean maybe there are public health issues which are often discussed in the country rather than water quality. This kind of sentence needs a citation and should not put in the conclusion.

Response: Revised.

Revision: you mean that your statistical analysis is able to find the causes? remember that regression is not able to find causality.

check this in Google: "Eight myths about causality and structural equation models"

Response: Revised.

Revision: change to recontamination

Response: Revised.

Revision: water treatment facilities (centralised water treatment) or household water treatment? they are two different things.

you write "significantly reduces" remember that you dont have any findings related to centralised water treatment (but you do recommend it in the discusion, which is fine).

Response: Revised.

Revision: should also educate or should be educated?

Response: Revised.

Revision: this is sentence is not clear. what exactly should be keep in mind?

Response: Revised.

Reviewer #2:

General:

The manuscript is technically sound. Though it has used secondary data, the data was analyzed vigorously with an appropriate use of statistics. Data supports conclusions that have been drawn by the author. The study incorporated an appropriate number of samples, and the methodology used for statistical analysis is latest and appropriate for the study.

Response: The authors thank the reviewer.

Major:

Comment: One of the aims of the paper is to monitor the empirical evidence to monitor the progress of the sustainable development targets in relation to the safe drinking water usage in Bangladesh. This aim was not achieved, or it is not clear how author intended to achieve this aim.

Response: The aim is revised.

Comment: In introduction, it is not mentioned, why this study is new and different from the existing studies. Identifying the research gap is not clear. Suggest authors to enumerate more on literature to highlight the gaps in the current research studies and the novelty of this work.

Response: The research gap is mentioned in the revised manuscript.

Comment: In results (statistical analysis), Adjusted Odds Ratio (AOR) interpretation was not done correctly. It is not clear on what basis; the author subtracts 1 from AOR values. Authors need to use the AOR values or explain with clear base why the subtraction is needed.

Response: The odds ratio for the reference category is 1.00. This one is subtracted from the other category to find the difference.

Comment: In results, when discussing regional changes of E. Coli values, “Mymenshing” division values must be used as it is the only lower value than the “Rangpur” division value which is the default. Or explain that it has been disregarded as the values are not significant.

Response: The reference category is selected based on the output from the bivariate analysis.

Comment: The conclusion is not addressed to the aims of the study fully.

Response: Revised.

Comment: Limitations of the study, recommendations to address for future works are not included which is also a major limitation of the paper.

Response: Limitation is included in the revised manuscript.

Minor:

Abstract:

Comment: 2nd para, 1,2,3 lines. Using of “if” is not appropriately done which alter the idea of the sentence.

Response: Revised

Introduction:

Comment: 2nd para, 2nd line. "Or other" written in brackets, it's not clear which other microbes the author tries to specify. It is better to name a microbe or maybe a family of microbes.

Response: Revised

Comment: 2nd para, 12th line. Better to start "Type of ma...." as a new paragraph as it discusses a new point.

Response: Revised

Comment: 2nd para, 14th line. "improved sources may have faecal contamination above the WHO standard" here above, does not give the idea that improved sources have minimum vulnerability to contaminate.

Response: Revised

Comment: 2nd para, 20th line. Better to start "A clear understanding ..." as a new paragraph as it discusses a new point.

Response: Revised

Comment: 2nd para, 31st line. It is unclear who "respondents" refers to.

Response: Revised

Variables: Predictor variables

Comment: 1st para, 4th line. "may be carried through the source of water collection", should be corrected as "may be occurred at the source of water collection"

Response: Revised

Statistical Analysis:

Comment: 1st para, 14th line. 35,36 references should be superscript.

Response: Revised

Results:

Comment: 1st para, 2nd line. reference not available

Response: Reference added.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

8 Apr 2022

Differences in levels of E. coli contamination of point of use drinking water in Bangladesh

PONE-D-21-23710R1

Dear Dr. Hoque,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Mentore Vaccari

Academic Editor

PLOS ONE

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Authors response well to my comments and have improved the draft. Thank you for all hard work. But since there are many new references in the draft, please check carefully all of them and make sure that the citation position/numbers arre correct.

Reviewer #2: It would be better if use percentages at least to interpret AOR values. Specially when you compares them to the standard value.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

20 Apr 2022

PONE-D-21-23710R1

Differences in levels of E. coli contamination of point of use drinking water in Bangladesh

Dear Dr. Hoque:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Professor Mentore Vaccari

Academic Editor

PLOS ONE