Tsukuba Economics Working Papers
No. 2023-002

The benefit of clean water on child health: An empirical analysis with specificreference to Escherichia Coli water contaminationbyAmmazia Hanif
Yuko Nakano
Midori Matsushima
March 2023

UNIVERSITY OF TSUKUBA
Department of Economics
1-1-1 Tennodai
Tsukuba, Ibaraki 305-8571
JAPAN

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Abstract
Microorganism-mediated degradation of water quality is a major public healthconcern in developing countries. Previous literature has shown an association between
household water pollution and childhood diarrhoea; however, its effects on child
growth and respiratory health have not been widely investigated. This study assessed
the effects of household drinking water contaminated with Escherichia coli (E. coli)
on child’s weight-for-height and weight-for-age z-scores, acute respiratory infections(ARI), and diarrhoea incidence among five years children in Pakistan. We used
district-level spatial information and the latest waves of unique Multiple Indicator
Cluster Survey (MICS) data containing information on ‘point-of-service delivery’(POS) and ‘point-of-consumption’ (POC) water quality, collected for the first time ona large scale in five regions of Pakistan. We employed an instrumental variableapproach to address potential endogeneity issues in household drinking water quality,
finding that POC drinking water contamination significantly affected children’s
weight-for-height and weight-for-age z-scores and ARI, in addition to its effects on
diarrhoea. The sub-sample analyses indicated that the effects of contaminated water
were particularly significant in children aged 6 months and older and in children who
did not receive vitamin A supplements. To protect the children from growth failureand contracting ARI and diarrhoea, household water quality should be improved.
Keywords: Child Health, Undernutrition, ARI, E. coli, Water quality, Pakistan

1Introduction
Ending undernutrition and reduction of infectious illness are critical prerequisites forsustainable development. Worldwide, around half of the under-five mortality (3 million) is
attributable to undernutrition (Black et al., 2013), which is linked to amplified risk ofmorbidity and infections including respiratory illness (Rudan et al., 2013; Liu et al., 2015).
Diarrhoea and acute respiratory infections (ARI) remain the leading causes of mortality andmorbidity, especially in developing countries (Paulson et al., 2021; Osarogiagbon & Isara,
2018). Every year, from one billion childhood diarrhoea episodes, around 5 million result indeath (Flückiger & Ludwig, 2022), and ARI contribute to 15% of deaths under the age of 5
worldwide. The cumulative health burden is evident because diarrhoea can impair child
growth (Khalil et al.,2018) and trigger ARI (Walker, Perin, Katz, Tielsch, & Black, 2013)
Recent research and international development partners have focused on
environmental contamination, which is a possible structural barricade to child development(Do, Joshi, & Stolper, 2018; Flückiger & Ludwig, 2022). Among environmental risks related
to health, water pollution in particular, is considered an ‘invisible threat’ to the global
development agenda, and contaminated water can significantly increase morbidity and affectlabour productivity and well-being (Devoto, Duflo, Dupas, Parienté, & Pons, 2012). E.coli
bacterial contamination1 was found to be one of the main causes of failure to meet the global
criteria for safely managing drinking water (Bain et al., 2021). Fecal exposure through poor
drinking water, sanitation and hygiene is linked to approximately 62% diarrhoeal mortality,
13% ARI burden and 16% malnutrition cases (Prüss-Ustün et al., 2019).

1

Escherichia coli (E. coli) - an indicator of faecal contamination is a species of faecal coliform group,
easily cultured microorganisms and used to measure the water quality level.

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Water pollution, malnutrition and infectious illness posing threat to the lives ofchildren are severe in Pakistan. Bacterial contamination is increasing due to rapid
urbanisation, water scarcity and poor sanitation. Access to safely managed drinking water isonly 35.8% (WHO & UNICEF, 2022) showing that even if the source water is safe,
unhygienic storage and water handling may lead to contamination (Julian, 2016). E. coli
presence indicates water exposure to animal or human faeces (Barnes, Anderson, Mumma,
Mahmud, & Cumming 2018; Santika et al., 2020; Usman, Gerber, & von Braun, 2019). As
access to basic sanitation is 68.4% in Pakistan, inadequate sanitation can elevate the risk of
microbial contamination due to extensive discharge of faecal sludge into water bodies(Ammazia, Yuko, & Midori, 2022 ; ; Zhang, 2012).
This study investigated the impact of E. coli contamination on weight-for-height andweight-for-age z-scores, ARI, and diarrhoea incidence in children up to the age of 5 years
using Multiple Indicator Cluster Survey (MICS) data in Pakistan, which has one of the worst
child health conditions in the world. MICS datasets have the unique feature of containing
information on ‘point of service delivery’ (POS) and ‘point-of-consumption’ (POC) water
quality, which was collected for the first time in a wide geographic area in Pakistan. This
allows us to directly examine the impact of POC water quality on child health. Moreover,having information on POS enables us to identify community-level source watercontamination. We use this community-level source water contamination as instrumental
variable (IV) to account for the possible endogeneity of POC drinking water quality throughour algorithm.
Our results document that a household using contaminated drinking water has ahigher probability of diarrhoeal episodes by 7.9 percentage points and raises the ARI risk
among children by 6.9 percentage points. Furthermore, our findings suggest that
contaminated water is correlated with a decrease in children’s weight-for-height by 0.31 and3weight-for-age by 0.32 standard deviations. Based on these results, we infer that E. coliinduced episodes of diarrhoea could be a risk factor for undernutrition and ARI. Clean
drinking water may improve respiratory health by preventing compromised immunity andmicronutrient deficiencies. In addition to diarrhoea, environmental enteric dysfunction(EED), a subclinical gut disorder triggered by relentless faecal-oral contamination, is anotherpossible route that associates water contamination with child growth (Modern et al., 2022;
Campbell et al., 2018).
In addition, we conducted two subsample analyses that measured the differential
effects of water quality on child health, focusing on children’s age and micronutrient
supplementation status. Dividing the sample by age group, we found that children aged 6months and older were susceptible to E. coli contamination, possibly due to a larger intakeof water and exposure to the contaminated environment than infants up to 6 months of age,
who are generally exclusively breastfed. Moreover, periodic Vitamin A supplementation
appears to protect children from contaminated disease environments as we found thatchildren who were not given vitamin A supplement doses were more likely to be affected bypoor water quality. These results suggest that vitamin A supplementation is an effectivemeans of mitigating the negative effects of contaminated water on children’s health andgrowth.
Our study makes two major contributions to this field. The first contribution has
epidemiological importance as it reveals the impact of E. coli contamination of POC wateron ARI and child growth. Although previous epidemiological studies have examined theimpact of water quality on the incidence of diarrhoea (Brown, Proum, & Sobsey, 2008;
Rahman, Kunwar, & Bohara, 2021; Usman et al., 2019; Khan, Hossain, Chakraborty, &Mistry, 2022; Hubbard et al., 2020; Ercumen et al., 2017; Fagerli et al., 2017; Luby et al.,2015; Gruber, Ercumen, & Jr, 2014; Goddard et al., 2020), height-for-age Z score (Goddard

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et al., 2020), self-reported maternal health (Kile et al., 2014), and child mortality (He &Perloff, 2016), the literature on the impact of E. coli contamination on ARI and child growthis scant. Since the evidence suggests that water, sanitation, and hygiene (WASH)
interventions and the choice of water sources affect child growth and ARI (Ashraf et al.,2020; Huda et al., 2012; Luby et al., 2018; Null et al., 2018; Fathmawati, Rauf, & Indraswari,2021; Shrestha, Vicendese, & Erbas, 2020; Swarthout et al., 2020), it is likely the case that
E.coli contamination is causing negative impact on child growth and occurrence of ARI
although some found no impacts (Swarthout et al., 2020). This is likely to be because ofdifferent nutritional status and different age of observation. The impact of E.colicontamination on child health, however, has not been well studied due to the lack of data on
POC water quality. In addition, we investigate the differential impact of E.coli contamination
on child health by age differences and Vitamin A intake. In doing so, this study provides
more detailed policy implications than previous studies did.
The second contribution of our study relates to our data set and identification
strategy. As discussed, MICS datasets have the unique feature of containing information on
POS and POC water quality. This data enables us to directly investigate the effectiveness of
POC contaminated water on child health by using community-level source watercontamination as IV, since POC drinking water quality could be endogenously determined.
This identification strategy provides robust results and adds evidence to the literature, as alimitation of previous studies is that they have largely ignored the endogeneity of householdwater quality. Although few exceptions are Rahman et al. (2021) and Usman et al. (2019),
who also took endogeneity issues into consideration and found that microbial watercontamination significantly affects the incidence of child diarrhoea, none of the otherprevious studies have done so.

5The reminder of this paper is organised as follows. In Section 2, we explain thedatasets. Section 3 outlines the empirical strategy. Section 4 documents the regression resultsof the analyses across several specifications, and Section 5 discusses the results. Finally,
Section 6 concludes the paper.

Data
Sampling
This study utilised data from the latest round of the Multiple Indicator Cluster Surveys(MICS) in Pakistan; a provincially representative cross-sectional survey followed a twostage stratified random sampling technique. We used the MICS survey waves from five
regions in the country from 2017 to 20212. During the survey, the first primary enumeration
areas were selected from rural and urban areas within each district. Subsequently, asystematic sample of 20 households was randomly selected from each enumeration area
(hereafter termed ‘cluster’). A unique feature of MICS is the addition of a new water quality
module developed by MICS and the Joint Monitoring Program (JMP) (UNICEF MICS,
2018) which was released as a part of latest round MICS6 .3 Worldwide, few datasets include
bacteriological water quality indicators at a national scale owing to collection challenges,
and in most cases, only the quality of piped water is measured. In our datasets, water quality
data at the POC and POS were collected separately for the first time on a large scale inPakistan using a universally accepted method (Khan et al., 2017).

2 Five survey waves include provinces MICS Punjab 2017–18, MICS Sindh 2018–19, MICS KPK 2019, MICS Baluchistan 2019–20 and a region MICSAJK 2020–21.

3

Methods were developed for direct water testing in MICS. Generally, laboratories conduct water
tests, which often suffer from logistic challenges in term of transportation and time frame.

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In the MICS datasets, 113,233 households were interviewed with information on
child anthropometry and other health outcomes from study regions of the country. For water
quality testing, 18,493 households were randomly selected. We limited our sample where
households gave consent for the water test; enumerators visited the source for E. coli test,
and information for both the source and household water test was available. For the analyses,
12,671 observations remained for children aged 0–60 months after cleaning the data.

Outcome variables
This study used four outcome variables. First, we used diarrhoea prevalence, which took thevalue of 1 if the child’s mother/caregiver reported the child had had diarrhoea 2 weeks before
the survey. Second, acute respiratory infections (ARI) symptoms were used, which took thevalue of 1 if the child had experienced rapid or difficult breathing along with a persistent
cough and the symptoms were associated with chest problems and a blocked nose 2 weeks
before the survey4. The third and fourth outcomes were child growth: weight-for-height andweight-for-age z-scores, which are the standard deviations of the anthropometry of the childbased on the growth standards of the WHO. Weight-for-age of less than -2 represents
underweight, while a child’s weight-for-height of less than -2 signifies wasting – a shortterm undernutrition indicator owing to acute weight loss.

Main explanatory variables
The key explanatory variable of POC-contaminated drinking water was a dummy variable,
which took 1 if E. coli was found in a household’s POC water5. The WHO drinking water

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Respiratory rate is a valuable clinical sign for diagnosing acute lower respiratory infections (e.g.
pneumonia and bronchiolitis) in children coughing and breathing rapidly.

5

The following precise question was asked: Could you please provide me with a glass of water thatmembers of your households usually drink?

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quality guidelines recommend that E. coli must not be detectable in 100-ml water samples(WHO, 2022. A water test was performed on the samples provided by the households within
30 minutes of collection followed by 24–48 hours of incubation. E. coli colonies werecounted as colony-forming units (CFU) /100 ml of water sample. Since each household use
water from different source, the source water sample was also collected for each household,and the E. coli contamination was calculated by using similar method used for householdwater test.
Fig. 1 shows the specific E. coli count (CFU/100 ml, ranging from 0 to 100 andabove) at the POC in the left panel and at the source in the right panel. In our sample, drinking
water of around 78% households was found contaminated with E. coli, and the percentage
of households having contaminated water at the source was around 59%. The variation in E.
coli concentration at the POC and source showed that a higher E. coli risk (100 or above
CFU) was prevalent at POC than at POS, suggesting that contamination occurs during theprocess of fetching and handling drinking water.
[Fig. 1]In the regression analysis, we controlled for other children, mothers and household
characteristics. Child-level variables included gender, age and prenatal check-ups. The
mother and household characteristics comprise the age and education of the mother andhousehold head, the household head’s gender, maternal smoking, household size, number ofchildren up to 5 years of age, cooking place, number of household members, household
assets, access to flush toilets as indicator of improved sanitation6, livestock ownership and

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Flush toilets reduced child weight loss, offer non-health welfare (e.g. time saving) and augments
satisfaction (Wang & Shen, 2022; Rahman et al., 2021).

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survey year dummy. The community-level variables included rural and urban dummies.
Appendix Table 1 presents the summary statistics for the variables used in the analyses.

District characteristics
We supplemented the information from the MICS with additional geospatial datasets toaccount for district-level characteristics that may affect health and water quality. Using rasterdatasets from QGIS, we constructed district-level data on rainfall, population density, slope
and temperature. Based on the district’s geocoded position, we link the MICS data to
information on district-level characteristics. For rainfall data, we used the Climate Hazards
Group InfraRed Precipitation with Station data 2.0 (CHIRPS) (Funk et al., 2015). For
population density, we used WorldPop data (WorldPop; Bondarenko & Maksym, 2020). The
district slope was calculated using ALOS DSM: Global 30m v3.2 data provided by the JAXA
Earth Observation Research Centre (EORC). For temperature, we used the dataset provided
by the NASA LP DAAC at the USGS EROS Centre. To control for the economic status ineach district, we used 3-arc-second (approximately 100 m at the equator) night-time lightdata constructed by NOAA’s National Centres for Environmental Information as a proxy. In
addition, we use malaria prevalence- a serious public health issue, as a proxy for malaria risk
(Plasmodium vivax parasite rate in general population). To obtain information on the averagemalaria prevalence rate, Malaria-Atlas map was used. We overlayed the map estimations oneach district location.
In Fig.2, using district-level spatial information, we mapped child health data fromthe sample and plotted the district-level risk prevalence of E. coli at POS and POC level andhealth-related outcomes. The figure shows that diarrhoea and ARI prevalence were higher
in areas with a high possibility of E. coli concentrations in POC water. A similar tendency
was observed for child growth indicators.

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[Fig 2]

Empirical strategy
This section describes the empirical strategy employed to estimate the impact of household
POC water quality on children’s health. A linear probability model (LPM) and two-stage
least squares (2SLS) were used. Following Rahman et al. (2021), we use Grossman’s (1972)
health production function approach as the motivation for our empirical estimation.
Following the conceptual framework of this approach, we estimate the model below, where
the health of child i is a function of the POC drinking water quality level and otherdemographic and socioeconomic factors:

Y𝑖𝑗𝑘𝑑 = 𝛽0 + 𝛽1 WQ 𝑗𝑘𝑑 + 𝛽2 H𝑖𝑗𝑘𝑑 + 𝛿𝑑 + 𝜃𝑑𝑟 + 𝜃𝑡 + 𝑒𝑖𝑗𝑘𝑑[1]

where 𝑌𝑖𝑗𝑘𝑑 refers to the health of child i of household j in cluster k of district d. Diarrhoea
prevalence, weight-for-height, weight-for-age z-score and ARI are the indicators of childhealth and the outcome variables of interest. 𝑊𝑄𝑗𝑘𝑑 is a dichotomous variable that denotes
POC water quality (presence of E. coli) in household j in cluster k. 𝐻𝑖𝑗𝑘𝑑 is a vector forcontrol variables at the household and individual levels. 𝛿𝑑 controls district-level
characteristics including population density, average rainfall and temperature during thesurvey year, average night-time light and slope of each district. 𝜃𝑑𝑟 controls for districttimes-rural fixed effects. Because rural and urban areas have different characteristics withineach district in terms of socioeconomic and health status, we include district-times-rural
fixed effects to account for any region-specific variability. 𝜃𝑡 is the time-fixed effect, while
𝑒𝑖𝑗𝑘𝑑 is the error term that explains the variation of 𝑌𝑖𝑗𝑘𝑑 . We clustered standard errors at the
MICS cluster level.
Since each household can decide whether to take any means to improve water
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quality, our model may suffer from a possible endogeneity problem. For instance, somehouseholds may have unobserved perceptions, preferences, behaviours, and awareness thatallow them to improve water quality and children’s health. If households are keen on health,for example, they could invest not only in improving water quality but also in other measures
to benefit their children’s health, which may cause bias in our estimates. To circumvent such
issues, we use 2SLS estimation and estimate the following regression equation as the firststage equation of the 2SLS estimation of equation 1:

WQ𝑗𝑘𝑑 = 𝛾0 + 𝛾1 SWQ 𝑘𝑑 + 𝛾4 H𝑖𝑗𝑘𝑑 + 𝛿𝑑 + 𝜃𝑑𝑟 + 𝜃𝑡 + 𝑣𝑗𝑘𝑑

[2]

where the excluded instrumental variable is community-level POS water contamination,
indicated as 𝑆𝑊𝑄𝑘𝑑 . As we discussed, in the MICS, the POS water quality data were
measured at the household level because each household used water from different sources.
As each household cannot change the quality of the water at the source, the POS water
quality should be largely exogenous for each household. There is, however, some possibilityof endogeneity because each household may be able to choose sources to take their water.
To circumvent this problem, we used non- self community level POS water contamination
by measuring the leave-out means7. If the community-level source contamination increases,
it would positively affect household water contamination. In addition, because the average
POS water contamination most likely affects children’s health mainly through drinking
water quality, the variable can satisfy the exclusion restriction, although there is no direct
way to test it. Therefore, we considered community-level POS water contamination as aplausible instrument for our study.

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Results
Descriptive statistics
In Table 1, we compare child health and growth outcomes among households with POC
drinking water with and without E. coli contamination. Table 1 shows that diarrhoea
prevalence is higher by 4.8% among children from households with contaminated drinking
water than households with E. coli-free water. The average weight-for-height and weightfor-age z-scores were lower among children from households exposed to E. colicontamination than among those using uncontaminated water. On average, the weight-forheight is -0.42 for children exposed to contaminated water and -0.32 for those who drink
water free of E. coli. We can observe a similar tendency for weight-for-age. The weight-forage of children with contaminated water was -1.32, while that of children withuncontaminated water was -1.10, and the difference was statistically significant. Further, theARI prevalence is also higher by 1.3% among children exposed to contaminated water thanamong households using uncontaminated water, although the difference is not statisticallysignificant.
[Table 1]
Fig 3 also shows dependence among E. coli and child health indicators. The left section
presents the positive association, on average, between diarrhoea and ARI prevalence and theE. coli presence in POC water. Which indicates better child health association with theabsence of E. coli in drinking water. When we consider child growth indicators, we observea negative association, on average, between weight-for-height and weight-for-age z-scoresand contaminated drinking water in the right section of Fig. 3. Which shows that improved
child growth was associated with the E. coli absence in drinking water.
[Fig.3]12

Main results
Estimates of the impact of POC E. coli in water on child diarrhoea, weight-for-height,
weight-for-age, and ARI prevalence are presented in Table 2. The estimated coefficients ofcontaminated water on weight-for-height and weight-for-age were negative and statisticallysignificant. The results show that the having E. coli in POC water decreases the child’s
weight-for-height and weight-for-age z-score by 0.067 and 0.076, respectively. In contrast,
the estimated coefficients from OLS were insignificant for diarrhoea and ARI prevalence
when we included district-times-rural fixed effects and time-fixed effects in the model.
Conversely, the estimated coefficients of contaminated water for weight-for-height andweight-for-age were negative and statistically significant.
[Table 2]
To circumvent this problem, we estimated the same model using community-level
POS water quality as IV for household-level POC water quality. The reduced- form
estimates in Appendix Table 2 suggest that households in a cluster with contaminated water
have a higher probability of diarrhoea incidence by 1.9 percentage points and of ARI by 1.6
percentage points than those having an uncontaminated water source. The estimatedcoefficients of the weight-for-height and weight-for-age z-scores are -0.074 and -0.077,
respectively, and are statistically significant.
Panel A of Table 3 shows the first-stage estimation results. The dependent variable
is POC water quality, while IV is community-level POS water contamination. The results
show a strong positive correlation between POC water quality and IV, and diagnostic tests
provide evidence of the validity of IV. For all outcomes, the null hypothesis for underidentification is rejected based on the LM version of the Kleibergen-Paap rk statistic.
In Panel B of Table 3, we estimate β1 by applying 2SLS that captures the impact of
POC water quality on child health outcomes. Column 1 shows that the probability of13

diarrhoea in children increases by 7.9 percentage points in households with contaminateddrinking water compared to those using uncontaminated water, this translates to 44 %increase in diarrhoea risk. The results for weight-for-height and weight-for-age as alternative
outcomes are also shown in columns 2 and 3. Consistent with childhood diarrhoea triggering
weight loss, particularly in the short run, we found that POC-contaminated drinking water
was associated with a decrease in children’s weight-for-height by 0.31 standard deviations
and their weight-for-age by 0.32 standard deviations. Column 4 shows that the probabilityof ARI risk among children increased by 6.9 percentage points in households with microbialcontaminated water. The IV estimates of the effect of water contamination on child healthare larger than those from OLS regressions and are all statistically significant. Althoughsome coefficients of the OLS estimates are insignificant, the direction of the impact is thesame as that of the IV estimates, suggesting the robustness of our results.
[Table 3]

Differential effects of E. coli contamination
In the previous estimation, we assumed that the effects of E. coli contamination are the sameacross all sample children. We further assessed whether the effects of POC water quality
varied across the different age groups as E. coli contamination may not similarly affect all
children. The results presented in Table 4 show stark differences in the impact ofcontaminated water on child health among different age groups. The IV results in columns
1–4 show the impact of water quality on the health of children aged 0–5 months, while
columns 5–8 show such impacts on children 6–60 months of age. The estimated coefficientsin columns 5-8 indicate that the link between E. coli-contaminated water and the risk ofdiarrhoea and ARI incidence was statistically significant among children older than 6months, with an average effect of 9.4 and 7.7 percentage points respectively. Moreover,14

contaminated water was significantly associated with a 0.34 and 0.35 standard deviation
decrease in weight-for-height and weight-for-age z-scores in children older than 6 months.
In contrast, the results in columns 1–4 show that water pollution does not affect children ofless than 6 months of age. This is plausible because the introduction of complementary
feeding and partial weaning from the age of 6 months increases a child’s exposure to acontaminated environment8. Children exclusively breastfed during the initial 6 months may
be protected naturally from pathogen exposure through contaminated food and water, having
less ARI, diarrhoeal morbidity and mortality (Mulatu et al., 2021; Arifeen et al., 2001;
VanDerslice et al., 1994;) and growth failure (Dharel et al., 2020). Although the results are
not shown, we found that exclusively breastfed children (aged 0–5 months) were notsignificantly affected by water contamination.
[Table 4]
In addition, we analysed whether the intake of vitamin A supplements mitigated thenegative effects of E. coli-induced risk of diarrhoea and ARI. Vitamin A deficiency (VAD)
remains a public health concern across the globe including in Pakistan, where its frequency
is more than 51%. According to previous studies, vitamin A (anti-infectious) is significant
for gastrointestinal and respiratory epithelial regeneration and reduces immune disorders,mortality, and morbidity among children aged 6–59 months (Kanakala et al., 2019; Imdad
et al., 2011; Barreto et al., 1994; WHO, 2013). Periodic vitamin A supplementation could
have the capacity to protect children by boosting their immune systems from contaminateddisease environments and related health risks (WHO, 2022) .

8

In terms of food intake for the previous day, the information was only solicited for children of up
to 2 years at the time of the survey.

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To examine these hypotheses, we conducted subsample analyses of the impact ofcontaminated water on child health and growth for those who were supplemented withvitamin A and those who were not. In our analyses, the vitamin A variable took the value of
1 if the child aged 6–60 months had been provided with vitamin A supplementation in theprevious 6 months. Table 5 shows the differential effects of contaminated water on the healthand growth of children with and without vitamin A supplementation. Columns 1–4 show theeffects of E. coli on the outcome variables for the vitamin A-supplemented group, while
columns 5–8 show the effects on the non-supplemented group. The results in columns 5 and8 show that contaminated water was statistically significantly associated with the risk ofdiarrhoea and ARI incidence, with an effect of 22 and 15 percentage points respectively, inthe non-vitamin A-supplemented group. Likewise, as shown in column 6, contaminatedwater was significantly associated with a 0.56 standard deviation decrease in children’s
weight-for-height in the non-vitamin A-supplemented group. In contrast, we did not observe
any significant effects of E. coli contamination on health among children supplemented withvitamin A, except for a negative coefficient for weight-for-age, for which we do not have aclear explanation. These results provide suggestive evidence to support our hypothesis thatmicronutrient supplementation can mitigate E. coli-induced health risks.
[Table 5]

Robustness checks
We conducted an array of robustness checks to document the validity and stability of ourresults. First, in Appendix Table 3, we examined whether the estimated 2SLS coefficientsremained robust after excluding extensive covariates. In the main models, we controlled forvariables related to the child, the mother, the household head, the household, district-level
characteristics, district-times-rural fixed effects and time-fixed effects. We gradually16

excluded district and household controls in columns 1, 2, 4, 5, 7, 8, 10 and 11, while we
included all these variables in columns 3, 6, 9 and 12 (the same as the main results shown in
Table 2). The results show that the coefficient of water quality remained largely steadydespite the inclusion or exclusion of various covariates.
Second, we added several variables which could affect water quality in the first- andsecond-stage regressions of our IV estimation. We included the means to extract water(motor, hand pump, and tube-well dummy), piped water dummy, water treatment status
dummy, handwashing dummy, flush toilet dummy and animal ownership. As these canpotentially determine water quality, excluding them may cause an omitted variable bias,
especially in the first-stage estimation. At the same time, however, these variables areendogenous, and thus, we excluded them from our main analyses. Appendix Table 4 shows
the first-stage and IV estimates with and without these variables. Column 1 in Panel A show
first-stage results of Table 2 and column 2 controls for the effect of potential water quality
determinants. Columns 1, 3, 5 and 7 in Panel B show the 2SLS results of Table 2, without
including variables that can determine water quality. In columns 2, 4, 6, and 8 of Panel B,
we include potential water quality determinants. The results show that the coefficients arealmost the same for the estimation results with and without these covariates, suggesting therobustness of the results.
Lastly, in Appendix Table 5, as a sensitivity analysis, we estimated our models bymeasuring the E. coli coliform as a continuous variable. Columns 1 and 4 show that a 1%
increase in E. coli (CFU/100 ml) in water increased the probability of diarrhoea by 0.014
percentage points and ARI by 0.013 percentage points. Such an increase in E. coli wasassociated with a decrease in children’s weight-for-height by 0.058 standard deviations andin their weight-for-age by 0.059 standard deviations; these effects were statistically17significant. Overall, the robustness tests suggested that the estimated findings are robust andconsistent.

Discussion
This study investigated the impact of household water quality on the incidence of childhood
diarrhoea, weight-for-height, weight-for-age and ARI using MICS data in Pakistan.
Globally, only a few datasets include bacteriological water quality indicators at the national
scale. MICS Pakistan is a unique data set that allows us to directly examine the impact of
POC water quality on children’s growth and ARI. Moreover, previous studies have largely
ignored the endogeneity of household drinking water quality, which might have caused bias
in their estimates. We addressed these concerns by employing the two-stage least squares
method and found that poor drinking water quality significantly and negatively affected child
growth, ARI, and increased the incidence of diarrhoea. In addition, we examined thedifferential impact of water quality on child health by age and vitamin A supplementationby conducting sub-sample analyses.
Our findings suggest that POC water contamination elevates the risk of malnutrition,
diarrhoea, and ARI. Negative impact of E. coli contamination on diarrhoea is consistent withprevious studies (e.g. Khan et al., 2022; Rahman et al., 2021; Usman et al., 2019; Luby et al.,2015), and we added evidence to the growing literature on negative impact of E. colicontamination on weight-for-height, weight-for-age, and ARI by employing rigorous
empirical estimation.
Age-based sub-sample analyses revealed that contaminated water significantly
affects child health indicators, particularly when a child turns 6 months or more. A reason
for this could be that after 6 months, children consume large quantities of water and are at ahigher risk of being infected through contact with contaminated water. It is possible that18

infants (0–5 months) are naturally protected by breastfeeding; thus, we did not observe asignificant negative impact of contaminated water on their health status. We also examinedthe differential impacts of water quality on child health depending on the provision of
micronutrient supplements to children aged 6–60 months. Our results suggest that bacterial
contamination did not have a significant effect on the incidence of diarrhoea, ARI, andweight-for-height z-score in children who were given vitamin A supplementation in theprevious 6 months, while their counterparts were significantly affected. These results imply
the effectiveness of vitamin A supplementation in mitigating the negative impacts ofcontaminated water on children’s health.
Despite our contributions, this study has two main limitations. First, water quality
was measured at a single point in time; therefore, we could only rely on cross-sectionalvariations to estimate the health impact of water quality. Undernutrition reflects theaccumulated effects of nutritional deficiency; therefore, a longitudinal study on the effectsof water quality on chronic undernutrition in children should be conducted in the future.
Second, we did not have information on the seasonal variability of water quality and E. coli
levels; thus, we could not examine whether rainy or dry seasons affect water contaminationand child health differently.

Conclusion
Pakistan is one of the countries that is most susceptible to respiratory infections anddiarrhoea-related mortality, and malnutrition in South Asia. This study examined the impactof microbial water contamination on childhood diarrhoea, weight-for-height, weight-forage, and ARI prevalence using data from the latest waves of MICS in five regions of Pakistan
during 2017–2021. Our key findings indicate that the presence of E. coli in POC water,which is generally used for drinking, significantly decreases a child’s weight-for-height and19weight-for-age and increases the risk of ARI and diarrhoea.
From our findings, we can highlight certain policy recommendations. First, financial
allocations should be diverted towards the provision of safely managed drinking water toimprove children’s health. For example, the establishment of waste-management treatment
plants and sewerage schemes may contribute to improving children’s health by reducing
water contamination. Second, families with children should realise the importance of a clean
environment for their growing children and that micronutrient supplementation andexclusive breastfeeding should be promoted. In Pakistan, currently, only approximately
37.7% of women exercise exclusive breastfeeding, and more than 50% of children sufferfrom vitamin A deficiency (National Nutrition Survey, 2018). The promotion ofbreastfeeding and supplementation with vitamin A would not only have a direct impact onchild health but also indirectly, by mitigating the negative impact of drinking watercontamination.
Considering that the issue of poor child health is getting severe possibly due to recent
massive floods in the country negatively affecting water safety and sanitation systems(UNICEF, 2022), it is necessary for us to conduct longitudinal analyses and observe longterm impacts of water contamination as well as how infrastructure and hygiene practice canprotect child health.
Acknowledgments
We would like to acknowledge the contributions of UNICEF in providing open access to the
MICS datasets. We are grateful for constructive comments from Hisahiro Naito, Fatwa
Ramdani, Kenichi Kashiwagi, Mari Minowa, Yu Zhengfei, Moges Abu Girma, Mohammad
Abdul Malek, Taiyo Fukai and Yoshinori Kurokawa.

References20

Arifeen, S., Black, R. E., Antelman, G., Baqui, A., Caulfield, L., & Becker, S. (2001).
Exclusive breastfeeding reduces acute respiratory infection and diarrhea deaths
among

infantsinDhaka

slums.

Pediatrics,

108(4),

E67.

https://doi.org/10.1542/peds.108.4.e67
Ashraf, S., Islam, M., Unicomb, L., Rahman, M., Winch, P. J., Arnold, B. F., BenjaminChung, J., Ram, P. K., Colford, J. M., & Luby, S. P. (2020). Effect of improved water
quality, sanitation, hygiene, and nutrition interventions on respiratory illness inyoung children in rural Bangladesh: a multi-arm cluster-randomized controlled trial.
The American Journal of Tropical Medicine and Hygiene, 102(5), 1124–1130.
https://doi.org/10.4269/ajtmh.19-0769
Bain, R., Johnston, R., Khan, S., Hancioglu, A., & Slaymaker, T. (2021). Monitoring
drinking water quality in nationally representative household surveys in low- andmiddle-income countries: cross-sectional analysis of 27 multiple indicator cluster
surveys 2014–2020. Environmental Health Perspectives, 129(9), 097010.
https://doi.org/10.1289/EHP8459
Barnes, A. N., Anderson, J. D., Mumma, J., Mahmud, Z. H., & Cumming, O. (2018). The
association between domestic animal presence and ownership and household
drinking water contamination among peri-urban communities of Kisumu, Kenya.
PloS One, 13(6), e0197587. https://doi.org/10.1371/journal.pone.0197587
Barreto, M. L., Santos, L. M., Assis, A. M., Araújo, M. P., Farenzena, G. G., Santos, P. A.,& Fiaccone, R. L. (1994). Effect of vitamin A supplementation on diarrhoea andacute lower-respiratory-tract infections in young children in Brazil. Lancet (London,
England), 344(8917), 228–231. https://doi.org/10.1016/s0140-6736(94)92998-x
Bashir, S., Aslam, Z., Niazi, N. K., Khan, M. I., & Chen, Z. (2021). Impacts of water quality
on human health in Pakistan. In M. A. Watto, M. Mitchell, & S. Bashir (Eds.), Water
Resources of Pakistan: Issues and Impacts (pp. 225–247). Springer International
Publishing. https://doi.org/10.1007/978-3-030-65679-9_12
Black, R. E., Victora, C. G., Walker, S. P., Bhutta, Z. A., Christian, P., de Onis, M., Ezzati,M., Grantham-McGregor, S., Katz, J., Martorell, R., & Uauy, R. (2013). Maternaland child undernutrition and overweight in low-income and middle-income
countries. The Lancet, 382(9890), 427–451. https://doi.org/10.1016/S01406736(13)60937-X

21

Brown, J. M., Proum, S., & Sobsey, M. D. (2008). Escherichia coli in household drinking
water and diarrheal disease risk: Evidence from Cambodia. Water Science andTechnology, 58(4), 757–763. https://doi.org/10.2166/wst.2008.439
Campbell, R. K., Schulze, K. J., Shaikh, S., Raqib, R., Wu, L. S. F., Ali, H., Mehra, S., West,K. P., & Christian, P. (2018). Environmental enteric dysfunction and systemic
inflammation predict reduced weight but not length gain in rural Bangladeshi
children.

BritishJournalof

Nutrition,

119(4),

407–414.

https://doi.org/10.1017/S0007114517003683
Devoto, F., Duflo, E., Dupas, P., Parienté, W., & Pons, V. (2012). Happiness on Tap: Piped
water adoption in urban Morocco. American Economic Journal: Economic Policy,
4(4), 68–99. https://doi.org/10.1257/pol.4.4.68
Do, Quy-Toan, Shareen Joshi, and Samuel Stolper. 2018. “Can Environmental Policy
Reduce Infant Mortality? Evidence from the Ganga Pollution Cases.” Journal of
Development Economics 133:306–25. doi: 10.1016/j.jdeveco.2018.03.001.
Dharel, D., Dhungana, R., Basnet, S., Gautam, S., Dhungana, A., Dudani, R., & Bhattarai,
A. (2020). Breastfeeding practices within the first six months of age in mid-westernand eastern regions of Nepal: A health facility-based cross-sectional study. BMC
Pregnancy and Childbirth, 20(1), 59. https://doi.org/10.1186/s12884-020-2754-0
Ercumen, A., Arnold, B. F., Naser, A. Mohd., Unicomb, L., Colford Jr, J. M., & Luby, S. P.
(2017). Potential sources of bias in the use of Escherichia coli to measure waterborne
diarrhoea risk in low-income settings. Tropical Medicine & International Health,22(1), 2–11. https://doi.org/10.1111/tmi.12803
Ercumen, A., Naser, A. M., Unicomb, L., Arnold, B. F., Jr, J. M. C., & Luby, S. P. (2015).
Effects of source- versus household contamination of tubewell water on childdiarrhea in rural Bangladesh: A randomized controlled trial. PLOS ONE, 10(3),e0121907. https://doi.org/10.1371/journal.pone.0121907
Fagerli, K., Trivedi, K. K., Sodha, S. V., Blanton, E., Ati, A., Nguyen, T., Delea, K. C.,
Ainslie, R., Figueroa, M. E., Kim, S., & Quick, R. (2017). Comparison of boilingand chlorination on the quality of stored drinking water and childhood diarrhoea inIndonesian households. Epidemiology and Infection, 145(15), 3294–3302.
https://doi.org/10.1017/S0950268817002217
Fathmawati, F., Rauf, S., & Indraswari, B. W. (2021). Factors related with the incidence ofacute respiratory infections in toddlers in Sleman, Yogyakarta, Indonesia: Evidence
22

from the Sleman Health and Demographic Surveillance System. PLOS ONE, 16(9),e0257881. https://doi.org/10.1371/journal.pone.0257881
Flückiger, M., & Ludwig, M. (2022). Temperature and risk of diarrhoea among children in
Sub-SaharanAfrica.

World

Development,

160,

106070.

https://doi.org/10.1016/j.worlddev.2022.106070
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G.,
Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards
infrared precipitation with stations—A new environmental record for monitoring
extremes. Scientific Data, 2(1), Article 1. https://doi.org/10.1038/sdata.2015.66
Getachew, A., Tadie, A., G.Hiwot, M., Guadu, T., Haile, D., G.Cherkos, T., Gizaw, Z., &Alemayehu, M. (2018). Environmental factors of diarrhea prevalence among underfive children in rural area of North Gondar zone, Ethiopia. Italian Journal of
Pediatrics, 44(1), 95. https://doi.org/10.1186/s13052-018-0540-7
Goddard, F. G. B., Pickering, A. J., Ercumen, A., Brown, J., Chang, H. H., & Clasen, T.
(2020). Faecal contamination of the environment and child health: A systematic
review and individual participant data meta-analysis. The Lancet. Planetary Health,4(9), e405–e415. https://doi.org/10.1016/S2542-5196(20)30195-9
Grossman, M. (1972). On the Concept of Health Capital and the Demand for Health. Journalof Political Economy. https://doi.org/10.1086/259880
Gruber, J. S., Ercumen, A., & Jr, J. M. C. (2014). Coliform bacteria as indicators of diarrheal
risk in household drinking water: Systematic review and reta-analysis. PLOS ONE,
9(9), e107429. https://doi.org/10.1371/journal.pone.0107429
Hanif, A., Nakano Y., & Matsushima M. (2022). The impact of access to improved sanitation
facilities on child health in Pakistan. Tsukuba Economics Working Papers No. 2022003. https://pepp.hass.tsukuba.ac.jp/RePEc/2022-003.pd
He, G., & Perloff, J. M. (2016). Surface water quality and infant mortality in China.
Economic

Development

andCultural

Change,65(1),

119–139.

https://doi.org/10.1086/687603
Hubbard, S. C., Meltzer, M. I., Kim, S., Malambo, W., Thornton, A. T., Shankar, M. B.,
Adhikari, B. B., Jeon, S., Bampoe, V. D., Cunningham, L. C., Murphy, J. L., Derado,G., Mintz, E. D., Mwale, F. K., Chizema-Kawesha, E., & Brunkard, J. M. (2020).
Household illness and associated water and sanitation factors in peri-urban Lusaka,

23

Zambia,

2016–2017.

Npj

Clean

Water,

3(1),Article1.

https://doi.org/10.1038/s41545-020-0076-4
Huda, T. M. N., Unicomb, L., Johnston, R. B., Halder, A. K., Yushuf Sharker, M. A., &Luby, S. P. (2012). Interim evaluation of a large-scale sanitation, hygiene and water
improvement programme on childhood diarrhea and respiratory disease in rural
Bangladesh.

SocialScience&

Medicine(1982),

75(4),

604–611.

https://doi.org/10.1016/j.socscimed.2011.10.042
Imdad, A., Yakoob, M. Y., Sudfeld, C., Haider, B. A., Black, R. E., & Bhutta, Z. A. (2011).
Impact of vitamin A supplementation on infant and childhood mortality. BMC Public
Health, 11(Suppl 3), S20. https://doi.org/10.1186/1471-2458-11-S3-S20
Julian, T. R. (2016). Environmental transmission of diarrheal pathogens in low- and middleincome countries. Environmental Science: Processes & Impacts, 18(8), 944–955.
https://doi.org/10.1039/C6EM00222F
Kanakala, M., Pediredla, K., Pachiappan, N., Ramayi, R. R., & Rehman, T. (2019). Effectof Vitamin A supplementation on preventing recurrent acute lower respiratory tract
infections in children. International Journal of Contemporary Pediatrics, 6(4),1632–1637. https://doi.org/10.18203/2349-3291.ijcp20192767
Khalil, I. A., Troeger, C., Rao, P. C., Blacker, B. F., Brown, A., Brewer, T. G., Colombara,
D. V., De Hostos, E. L., Engmann, C., Guerrant, R. L., Haque, R., Houpt, E. R.,
Kang, G., Korpe, P. S., Kotloff, K. L., Lima, A. A. M., Petri, W. A., Platts-Mills, J.
A., Shoultz, D. A., … Mokdad, A. H. (2018). Morbidity, mortality, and long-term
consequences associated with diarrhoea from Cryptosporidium infection in children
younger than 5 years: A meta-analyses study. The Lancet Global Health, 6(7), e758–
e768. https://doi.org/10.1016/S2214-109X(18)30283-3
Khan, J. R., Hossain, Md. B., Chakraborty, P. A., & Mistry, S. K. (2022). Household
drinking water E. coli contamination and its associated risk with childhood diarrhea
in Bangladesh. Environmental Science and Pollution Research, 29(21), 32180–
32189. https://doi.org/10.1007/s11356-021-18460-9
Khan, S. M., Bain, R. E. S., Lunze, K., Unalan, T., Beshanski-Pedersen, B., Slaymaker, T.,
Johnston, R., & Hancioglu, A. (2017). Optimizing household survey methods tomonitor the Sustainable Development Goals targets 6.1 and 6.2 on drinking water,
sanitation and hygiene: A mixed-methods field-test in Belize. PLOS ONE, 12(12),e0189089. https://doi.org/10.1371/journal.pone.0189089

24

Kile, M. L., Rodrigues, E. G., Mazumdar, M., Dobson, C. B., Diao, N., Golam, M.,
Quamruzzaman, Q., Rahman, M., & Christiani, D. C. (2014). A prospective cohort
study of the association between drinking water arsenic exposure and self-reported
maternal health symptoms during pregnancy in Bangladesh. Environmental Health,13(1), 13-29. https://doi.org/10.1186/1476-069X-13-29
Koolwal, G., and D. Van de Walle. 2013. “Access to Water, Women’s Work, and Child
Outcomes.” Economic Development and Cultural Chang,e 61 (2): 369–405
Luby, S. P., Halder, A. K., Huda, T. M., Unicomb, L., Islam, M. S., Arnold, B. F., &Johnston, R. B. (2015). Microbiological contamination of drinking water associatedwith subsequent child diarrhea. The American Journal of Tropical Medicine andHygiene, 93(5), 904–911. https://doi.org/10.4269/ajtmh.15-0274
Luby, S. P., Rahman, M., Arnold, B. F., Unicomb, L., Ashraf, S., Winch, P. J., Stewart, C.
P., Begum, F., Hussain, F., Benjamin-Chung, J., Leontsini, E., Naser, A. M., Parvez,
S. M., Hubbard, A. E., Lin, A., Nizame, F. A., Jannat, K., Ercumen, A., Ram, P. K.,… Colford, J. M. (2018). Effects of water quality, sanitation, handwashing, andnutritional interventions on diarrhoea and child growth in rural Bangladesh: A cluster
randomised controlled trial. The Lancet Global Health, 6(3), e302–e315.
https://doi.org/10.1016/S2214-109X(17)30490-4
Modern, G., Mpolya, E., & Sauli, E. (2022). Causal relationship between environmental
enteric dysfunction (EED), poor WaSH practices and growth failure in children from
Rukwa-Tanzania.

Scientific

African,16,

e01281.

https://doi.org/10.1016/j.sciaf.2022.e01281
Mulatu, T., Yimer, N. B., Alemnew, B., Linger, M., & Liben, M. L. (2021). Exclusive
breastfeeding lowers the odds of childhood diarrhea and other medical conditions:
Evidence from the 2016 Ethiopian demographic and health survey. Italian Journalof Pediatrics, 47(1), 166. https://doi.org/10.1186/s13052-021-01115-3
National Nutrition Survey 2018. Key Findings Report. (n.d.). Retrieved December 27, 2022,
fromhttps://www.unicef.org/pakistan/reports/national-nutrition-survey-2018-key-

findings-report
Null, C., Stewart, C. P., Pickering, A. J., Dentz, H. N., Arnold, B. F., Arnold, C. D.,
Benjamin-Chung, J., Clasen, T., Dewey, K. G., Fernald, L. C. H., Hubbard, A. E.,
Kariger, P., Lin, A., Luby, S. P., Mertens, A., Njenga, S. M., Nyambane, G., Ram,
P. K., & Colford, J. M. (2018). Effects of water quality, sanitation, handwashing, and25

nutritional interventions on diarrhoea and child growth in rural Kenya: A clusterrandomised controlled trial. The Lancet Global Health, 6(3), e316–e329.
https://doi.org/10.1016/S2214-109X(18)30005-6
Osarogiagbon, W. O., & Isara, A. R. (2018). Knowledge of acute respiratory infection inunder-fives and homebased practices by their caregivers in an urban community insouthern Nigeria. African Journal of Thoracic and Critical Care Medicine, 24(4).
https://doi.org/10.7196/AJTCCM.2018.v24i4.188
Paulson, K. R., Kamath, A. M., Alam, T., Bienhoff, K., Abady, G. G., Abbas, J., AbbasiKangevari, M., Abbastabar, H., Abd-Allah, F., Abd-Elsalam, S. M., Abdoli, A.,
Abedi, A., Abolhassani, H., Abreu, L. G., Abu-Gharbieh, E., Abu-Rmeileh, N. M.,
Abushouk, A. I., Adamu, A. L., Adebayo, O. M., … Kassebaum, N. J. (2021).
Global, regional, and national progress towards Sustainable Development Goal 3.2
for neonatal and child health: All-cause and cause-specific mortality findings fromthe Global burden of disease study 2019. The Lancet, 398(10303), 870–905.
https://doi.org/10.1016/S0140-6736(21)01207-1
Prüss-Ustün, A., Wolf, J., Bartram, J., Clasen, T., Cumming, O., Freeman, M. C., Gordon,B., Hunter, P. R., Medlicott, K., & Johnston, R. (2019). Burden of disease frominadequate water, sanitation and hygiene for selected adverse health outcomes: An
updated analysis with a focus on low- and middle-income countries. International
Journalof

HygieneandEnvironmental

Health,222(5),

765–777.

https://doi.org/10.1016/j.ijheh.2019.05.004
Rahman, M. M., Kunwar, S. B., & Bohara, A. K. (2021). The interconnection between water
quality level and health status: An analysis of Escherichia Coli contamination anddrinking water from Nepal. Water Resources and Economics, 34, 100179.
https://doi.org/10.1016/j.wre.2021.100179
Rudan, I., O’Brien, K. L., Nair, H., Liu, L., Theodoratou, E., Qazi, S., Lukšić, I., Fischer
Walker, C. L., Black, R. E., Campbell, H., & Child Health Epidemiology ReferenceGroup (CHERG). (2013). Epidemiology and etiology of childhood pneumonia in
2010: Estimates of incidence, severe morbidity, mortality, underlying risk factors
and causative pathogens for 192 countries. Journal of Global Health, 3(1), 010401.
https://doi.org/10.7189/jogh.03.010401
Santika, N. K. A., Efendi, F., Rachmawati, P. D., Has, E. M. M., Kusnanto, K., & Astutik,
E. (2020). Determinants of diarrhea among children under two years old in Indonesia.

26Children

and

Youth

Services

Review,

111,

104838.

https://doi.org/10.1016/j.childyouth.2020.104838
Shrestha, S. K., Vicendese, D., & Erbas, B. (2020). Water, sanitation and hygiene practices
associated with improved height-for-age, weight-for-height and weight-for-age zscores among under-five children in Nepal. BMC Pediatrics, 20(1), 134.
https://doi.org/10.1186/s12887-020-2010-9
Swarthout, J., Ram, P. K., Arnold, C. D., Dentz, H. N., Arnold, B. F., Kalungu, S., Lin, A.,
Njenga, S. M., Stewart, C. P., Colford, J. M., Null, C., & Pickering, A. J. (2020).
Effects of individual and combined water, sanitation, handwashing, and nutritional
interventions on child respiratory infections in rural Kenya: A cluster-randomized
controlled trial. The American Journal of Tropical Medicine and Hygiene, 102(6),1286–1295. https://doi.org/10.4269/ajtmh.19-0779
UNICEF (2018). Methodological Work—UNICEF MICS. Retrieved December 9, 2022,
from https://mics.unicef.org/methodological-work
UNICEF (2022). Devastating floods in Pakistan. Retrieved November 26, 2022, fromhttps://www.unicef.org/emergencies/devastating-floods-pakistan-2022
Usman, M. A., Gerber, N., & von Braun, J. (2019). The impact of drinking water quality andsanitation on child health: Evidence from rural Ethiopia. Journal of Development
Studies, 55(10), 2193–2211. https://doi.org/10.1080/00220388.2018.1493193
VanDerslice, J., Popkin, B., & Briscoe, J. (1994). Drinking-water quality, sanitation, andbreast-feeding: Their interactive effects on infant health. Bulletin of the World HealthOrganization,

72(4),

589–601.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2486614/
Wan, Zhengming, Hook, Simon, & Hulley, Glynn. (2021). MODIS/Terra Land Surface
Temperature/Emissivity Daily L3 Global 1km SIN Grid V061 [Data set]. NASA
EOSDIS Land Processes DAAC. https://doi.org/10.5067/MODIS/MOD11A1.061
Wang, D., & Shen, Y. (2022). Sanitation and work time: Evidence from the toilet revolution
in

rural

China.

World

Development,

158,

105992.

https://doi.org/10.1016/j.worlddev.2022.105992
Walker, C. L. F., Perin, J., Katz, J., Tielsch, J. M., & Black, R. E. (2013). Diarrhea as a risk
factor for acute lower respiratory tract infections among young children in lowincome

settings. Journalof

https://doi.org/10.7189/jogh.03.010402

27

globalhealth, 3(1),010402.

WorldPop, & Bondarenko, Maksym. (2020). Individual countries 1km population density(2000-2020)

[Data

set].

UniversityofSouthampton.

https://doi.org/10.5258/SOTON/WP00674
WHO (2022). Action—Nutrition International—Pakistan—Vitamin A supplementation |Global database on the Implementation of Nutrition Action (GINA). Retrieved
December 25, 2022, from https://extranet.who.int/nutrition/gina/en/node/26182
WHO (2022). Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the Firstand Second Addenda. Retrieved March 9, 2023 (https://www.who.int/publicationsdetail-redirect/9789240045064).
WHO(2013)

Micronutrient

deficiencies.

Retrievedfromhttps://www.who.int/nutirtion/topics/vad/en/.
WHO & UNICEF (2022) Data of Joint Monitoring Program. Retrieved December 5, 2022,
from https://washdata.org/data.
Zhang, J. (2012). The impact of water quality on health: Evidence from the drinking water
infrastructure program in rural China. Journal of Health Economics, 31(1), 122–134.
https://doi.org/10.1016/j.jhealeco.2011.08.008

28

Table 1. Mean comparison tests for child health outcomes with and without E. coli inhousehold drinking water.
Household POC water quality (E. coli)
Variables

Contaminated Uncontaminated Totalt-testDiarrhoea

0.187

0.139

0.178

-0.048***Weight-for-height-0.424-0.324-0.4030.101***

Weight-for-age-1.325-1.109-1.2810.217***

ARI

0.099

0.086

0.096

-0.012*

Observations

10,056

2,615

12,671

Notes: Authors’ calculations. * p < 0.10, ** p < 0.05, *** p < 0.01. Test for the mean comparison
between households consuming water uncontaminated and contaminated with E. coli.

29

Table 2. OLS estimates of the effects of drinking water quality on child health.
Dependent variables
Water quality (1=E.coli present)
Log of population
Night-time light
RainfallTemperature
SlopeChild, mother, HH head, and HH controls
District x Rural FE
Year FE

Diarrhoea(1)
0.008(0.010)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Weight-for- Weightheight
for-age(2)(3)-0.067*-0.076**(0.040)(0.036)

ARI
(4)
0.004
(0.007)

Yes
Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes
Yes

Yes
Yes

Yes
Yes

Yes
Yes

Clustered standard errors (robust) at the MICS cluster level are in parentheses; *** p < 0.01, **p < 0.05, * p < 0.1. n = 12,671

30

Table 3. The IV estimates the effects of drinking water quality on child health.
Regression stage
Panel A. First-stage regression
Community-levelsourcewater
contamination (except self)
Control variables
Child, mother characteristics
Household Characteristics
District Characteristics
District x rural FE
Year FE
Weak identification test
Kleibergen-Paap rk Wald F-statistics
Dependent variables
Panel B. Second-stage regression
Water quality (1=E. coli present)

Water quality (1=E.coli present)
(2)
(3)

(1)

(4)

0.252***(0.0165)0.248***(0.0165)0.246***(0.0165)0.241***(0.0165)

Yes

Yes
Yes

Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

Diarrhoea(1)Weight-forheight(2)

Weightfor-age
(3)

ARI
(4)

0.079*(0.044)-0.312*(0.171)-0.322**(0.151)

0.069**(0.035)

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes

212.089

Control variables
Child, mother characteristics
Household Characteristics
District Characteristics
District x Rural FE
Year FE

Clustered standard errors (robust) at MICS cluster level are in parentheses; *** p < 0.01, ** p < 0.05,* p < 0.1. The endogenous variable household POC water quality is instrumented by communitylevel source water contamination. n = 12,671

31

Table 4. Differential effects of drinking water quality on child health by age group

Variables

Younger than 6 months of age (2SLS)
Weight-forWeight-forDiarrhoea
Height
Age
(1)
(2)
(3)

ARI
(4)

Aged 6 months and older (2SLS)
Weight-forWeight-forDiarrhoea
Height
Age
(5)
(6)
(7)

ARI
(8)

Water quality (1= E. coli -0.038
present)(0.126)-0.356

0.015

0.012

0.094*-0.349**-0.358**0.0771**(0.506)(0.435)(0.089)(0.046)(0.178)(0.158)(0.036)

Observations

1,270

1,270

1,270

11,401

11,401

11,401

11,401

1,270

Districts controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Child, mother, HH
head, and HH controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

District x Rural FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year FE
Yes
Yes
Yes
Yes
Yes
Yes
F-Statistics
47.3
Clustered standard errors (robust) at MICS cluster level are in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

Yes

Yes

32

206.4

Table 5. Differential effects of drinking water quality on child health depending on Vitamin A supplementation
Vitamin A-Supplemented group (2SLS)(1)Weight-forHeight(2)

Weight-forAge(3)-0.006-0.159(0.059)Observations

Vitamin A Non- Supplemented group (2SLS)(5)Weight-forHeight(6)

Weight-forAge
(7)

0.023

0.221***-0.569**-0.3250.150***(0.213)(0.049)(0.072)(0.269)(0.234)(0.052)

5,855

5,855

5,855

5,546

5,546

5,546

5,546

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Child, mother, HH
head, and HH controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

District x Rural FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes
98.41

Yes

Variables

ARI

Diarrhoea(4)-0.420*(0.212)

5,855

Districts controls

Water quality (1= E. coli
present)

Diarrhoea

Year FE
Yes
Yes
Yes
Yes
Yes
F-Statistics
127.5
Clustered standard errors at MICS cluster level are in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1.

33

ARI
(8)

Appendix Table 1. Summary Statistics
Variables

Mean

Std. Dev.

Min

Max

HH water quality (1= E. coli present)

0.793

0.404

0

1

Diarrhoea

0.178

0.382

0

1

Weight-for-height z-score-0.403

1.360

-5

5

Weight-for-age z-score-1.2816

1.301

-5.99

4.94

ARI

0.096

0.295

0

1

Child gender (1 = male)

0.514

0.499

0

1

Child age (month)

30.06

17.07

0

60

Child prenatal checks (4 or more)

0.609

0.487

0

1

Children under 5 years

2.399

1.329

1

10

HH head gender (1 = male)

0.924

0.263

0

1

HH head education

0.500

0.500

0

1

Maternal education

0.395

0.489

0

1

Maternal age (1 = < 20,…,4 = > 35)

2.559

0.847

1

4

Maternal smoking status

0.020

0.141

0

1

Wealth index quantile (1 = poorest,..., 5 = richest)

2.657

1.365

1

5

Number of HH members

9.391

4.920

2

42

Cooking place (1 = outdoor)

0.281

0.449

0

1

Flush toilet

0.469

0.499

0

1

Animal ownership

0.539

0.498

0

1

Residential area (1 = rural)
Year
District level controls

0.781
2018

0.412
0.773

0
2017

1
2021

Log of population

5.782

1.481

1.107

10.847

Malaria prevalence rate

0.007

0.007

0.0001

0.037

Night-time light

1.242

5.440

0.072

48.730Rainfall515.594380.485

44.8601556.3Temperature

31.09

6.188

8.902

41.979Slope
Note: Sample size = 12,671

6.963

8.706

0.281

34.355

HH Demographic and Socioeconomic

34

Appendix Table 2. Results of reduced-form regression for IV estimation

Variables
Community
sourcequality (except self)

Diarrhoea(1)

Dependent Variable
WeightforWeight-forheight
age
(2)
(3)

0.0190*(0.010)
Yes
Yes
Yes
Yes
Yes

-0.0748*(0.0418)
Yes
Yes
Yes
Yes
Yes

ARI
(4)

water
-0.0770**(0.0365)
Yes
Yes
Yes
Yes
Yes

0.016**(0.00857)
Yes
Yes
Yes
Yes
Yes

Log of population
Night-time light
RainfallTemperature
SlopeChild, mother, HH head, and
Yes
Yes
Yes
Yes
HH controls
District x rural FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Year FE
Clustered standard errors (robust) at the MICS cluster level are in parentheses; *** p < 0.01, **p < 0.05, * p < 0.1. n = 12,671.

35

Appendix Table 3. Sensitivity analysis excluding covariates from the 2SLS estimation modelfor the impact of drinking water quality on child health.
Variables(1)
Water quality (1 = E. coli present)

Diarrhoea(2)(3)

0.077* 0.083* 0.079*(0.042) (0.042) (0.044)
Weight-for-age(7)(8)
0.301** -0.296**(0.149) (0.148)

Weight-for-height
(4)
(5)
(6)
-0.346**(0.16)-0.346**(0.167)
ARI

0.312*(0.171)

(9)

(10)

(11)

(12)

-0.322**(0.152)
Yes
Yes
Yes

0.059*(0.034)0.060*(0.034)0.069**(0.035)
Yes
Yes
Yes

District characteristics
Household controls
Yes
Yes
Child, mother, HH head
Yes
Yes
Yes
Yes
controls
Yes
Yes
Year FE
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
District x rural FE
F-Statistics
225.1
220.9
212.0
225.1
220.9
212.0
Notes: Clustered standard errors (robust) at the MICS cluster level are in parentheses; *** p < 0.01, **p < 0.05, * p < 0.1. n = 12,671

36

Appendix Table 4. Sensitivity analysis with and without WASH infrastructure and hygiene
behaviour-related variables for 2SLS estimates of the impact of drinking water quality on
child healthWithout waterWith water quality
quality determinant
determinantvariables
variables
Water quality (1 = E. coli present)(1)(2)

Panel A. First-stage regression

0.241***(0.0165)
Diarrhoea(1)(2)
0.079*
0.074*(0.044)(0.044)Weight-for-age(5)(6)-0.322**-0.337**(0.152)(0.153)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Community-level source water contamination
Panel B. Second-stage regression
Water quality (1 = E. coli present)

Piped water
Motorized pump/borehole
Tube well
Water treatment
Handwashing
Flush toilet
Animal ownership
Other controls (all)0.239***(0.0164)Weight-for-height(3)(4)-0.312*-0.326*(0.171)(0.171)ARI(7)(8)
0.069*
0.067*(0.035)(0.035)
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

Clustered standard errors (robust) at the MICS cluster level are in parentheses; *** p < 0.01, ** p <0.05, * p < 0.1. Access to flush toilets and animal ownership are included as control variables. Columns
1,3,5 and 7 in panel B presents the results of 2SLS similar to Table. 3. The regressions results presented
in Columns 2,4,6 & 8 controls for potential factors that can significantly affect microbial water quality.
n= 12,671.

37

Appendix Table 5: Impact of drinking water quality (measured as log of E. coli [CFU/100 ml])on child health.

Variables
Water quality(Log [E. coli measured in CFU/100ml])

Diarrhoea(1)Weightfor-height(2)

0.0147*(0.008)-0.058*(0.031)

Weightfor-age
(3)

ARI
(4)

-0.059**0.013**(0.028)(0.006)
F-Statistics
362.3
Controls (all)
Yes
Yes
Yes
Yes
Clustered standard errors (robust) at the MICS cluster level are in parentheses; *** p < 0.01, ** p < 0.05, *p < 0.1. n = 12,671.

38

Figure 1. Point of consumption and source water quality status (E. coli [CFU/100 ml])39

Figure 2. Microbial contamination and child health status at the district level40Figure 3. Relationship between child health indicators and E. coli contamination

41