Addressing Fecal
Contamination in Rural Kenyan Households:
The Roles of Environmental Interventions and Animal Ownership
Diarrheal disease remains a leading cause
of under-5 child mortality
in low- and middle-income countries (LMICs). Chronic diarrhea is also associated with stunting (length-for-age Z -scores >2 standard deviations below the median of the
World Health Organization (WHO) growth standard for age and gender);
stunting affected an estimated 144 million children under 5 years
globally in 2020 and is targeted under Goal 2 (Zero Hunger) of the
Sustainable Development Goals. − The Sustainable Development Goals
have encouraged significant investments to increase access to safe
drinking water, sanitation, and handwashing (WSH) and reduce the burden
of infectious disease by 2030. WSH interventions
are hypothesized to interrupt the transmission of fecal pathogens
in the household environment, thereby reducing the risk of diarrhea. Previous studies have predominately used observational
data to study associations between fecal indicator bacteria in the
household environment (e.g., in water, on hands, in soil, on surfaces),
child health outcomes (e.g., diarrhea, stunting), and the quality
of WSH infrastructure. − However, the ability to infer causal effects from observational
studies is limited, as study participants cannot be assigned to randomized
treatment and control groups; the lack of randomization hinders the
capability to dismiss potential effects caused by unmeasured confounders. A large number of randomized controlled trials (RCTs) have assessed
the effects of water treatment interventions on fecal indicator bacterial
levels in drinking water, but few sanitation and handwashing RCTs
have measured environmental indicators of fecal contamination. , We identified two RCTs evaluating the effects of handwashing interventions
on hand contamination, neither of which reduced bacterial levels in
hands. , Due to high temporal heterogeneity in hand
contamination, random hand rinse samples, as were collected in these
two RCTs, may be poor proxy measures for handwashing behavior around
critical events for bacterial transmission (e.g., eating). , A 2016 systematic review concluded there was no evidence that improved
sanitation reduces fecal contamination levels in water, on hands,
on sentinel toys, on household surfaces, or in soil, although inherent
heterogeneity of settings, interventions and their adoption/coverage,
and methods between studies prevented the authors from conducting
meta-analyses; only one study included in the review (Clasen et al.)
was an RCT that simultaneously measured microbiological indicators
across multiple pathways (water, hand rinses, toy rinses, flies). , Recent RCTs have also demonstrated mixed effects of WSH interventions
on diarrhea and stunting. − It is therefore hypothesized
(partially based on previous observational and modeling studies) that
combining WSH interventions, rather than delivering them in isolation
as has been done in most previous trials, could improve microbial
contamination and promote health benefits. − The lack of observed effects on child health outcomes could
partially
be explained by insufficient adherence to interventions or community-level
coverage of interventions, while an additional explanation could be
the potential negative health effects of exposure to animal feces,
which has historically been neglected in the design of WSH interventions. − Previous research has shown that animal feces contribute to fecal
contamination in the domestic environment. A study in rural Bangladesh
(Bangladesh WASH Benefits study) found that Escherichia
coli concentrations were higher in soil, stored water,
and food sampled from compounds with animals compared to those without
animals. Host-specific fecal markers
from animals (dogs, poultry, ruminants) were detected in multiple
household reservoirs (soil, hands, stored water) in rural and urban
Bangladesh, rural India, and rural Kenya. − In all sample types tested, animal fecal markers were more prevalent
than human fecal markers. In rural Bangladesh, increased concentration
of an animal-specific fecal marker (BacCow) was associated with increased
prevalence of pathogenic E. coli . Allowing animals to freely roam and graze is
a common animal husbandry practice in many low-income communities.
Nonanimal-owning households might therefore also be at a high risk
of exposure to fecal contamination from animal feces. Previous work
in Bangladesh has documented ruminant-specific fecal markers on child
hands or floor sponge samples in both goat-owning and nonruminant-owning
compounds. However, as most animal ownership
studies have focused on household-level ownership, little is known
about the impact of community-level ownership on contamination in
domestic environments. While animal ownership can put humans
at an elevated risk of exposure
to animal fecal contamination, current evidence suggests that rural
household livestock ownership likely provides child nutritional benefits. − This might partially be due to livestock ownership directly increasing
household consumption of animal-sourced foods. Socioeconomic pathways
might also be important. For example, large livestock (e.g., cattle)
ownership could be a proxy for higher household socioeconomic status.
Small livestock (e.g., poultry) ownership could increase women’s
empowerment; as women are often the primary caretakers of household
poultry, greater participation in livestock markets might increase
women’s control over making purchasing decisions for child
caretaking. High-intensity animal exposure
(e.g., housing livestock in child sleeping quarters), however, has
been linked to poor child health outcomes, including diarrheal disease,
environmental enteric dysfunction (EED), and growth faltering. , , There is thus a need to contextualize
the role of animals in domestic fecal contamination alongside environmental
intervention trials to understand whether interventions may reduce
child exposure to human and animal feces, without introducing barriers
to nutritional benefits from household animal ownership. The
Kenya WASH Benefits study was a cluster-randomized controlled
trial designed to test the effects of water, sanitation, handwashing,
and nutritional interventions, alone and in combination, on child
diarrhea prevalence, linear growth, parasitic infections, biomarkers
of EED, and child development. , − The trial’s primary outcomes have been reported: WSH interventions,
whether separately or in combination, did not reduce child diarrhea
or improve child growth during the trial. To assess the extent to which the interventions may have reduced
child exposure to fecal contamination—a key intermediate step
to health outcomes—we nested environmental sample collection
within a subset of enrolled households in selected trial arms. Our
aim was to determine if the interventions reduced levels of fecal
indicator bacteria in the domestic environment along likely exposure
pathways for young children. Further, we leveraged data from the study
to contextualize the role of animals in domestic contamination by
assessing whether household-level animal ownership was associated
with (1) higher E. coli prevalence
and concentrations in the domestic environment (stored water, child
hand rinses, sentinel toy rinses) and fly prevalence and densities
near food preparation areas; and (2) under-5 child health outcomes
(diarrhea, stunting). We also assessed whether community-level (village-level)
animal ownership was associated with environmental contamination and
child health outcomes. 

Study Design The Kenya WASH Benefits trial enrolled
pregnant women in Kakamega, Bungoma, and Vihiga counties of rural
Western Kenya and followed the children born to these pregnancies
(including twins) for 2 years. Households in this region are often
organized into compounds, where multiple related families live together
in a defined area and share latrines and communal areas for cooking,
child play, and domestic animal grazing/housing. A random number generator
with reproducible seed was used to assign interventions to household
clusters; groups of nine geographically adjacent clusters were block-randomized
into a passive control arm, active control arm, or one of six intervention
arms (chlorinated drinking water; improved sanitation through pit
latrine upgrades with a reinforced slab and drop hole cover, child
potties, and scoops for removing human and animal feces from the homes
and yards of enrolled children; handwashing with soap; combined WSH
interventions; small quantity lipid-based nutrient supplementation;
combined WSH and nutritional (WSHN) interventions). Study design details
for the trial, including details regarding the passive control arm,
active control arm (including monthly visits by community health promoters),
six intervention arms, and behavior change messaging through monthly
visits for all intervention arms (including infant and young child
feeding counseling for the nutrition and WSHN arms) were previously
published ( Supporting Information S1 and Figure S1 ). , , Sample and Data Collection and Processing Approximately
1 year and 2 years after intervention delivery (timed to match the
collection of the trial’s primary child health outcomes), we
assessed environmental contamination in a subset of approximately
1500 households (approximately 375 children from each of the control,
nutrition, combined WSH, and combined WSHN arms) that participated
in EED biomarker measurement and comprised the EED cohort of the trial
. The intensive
environmental contamination assessment was conducted in the EED subgroup
because of the added benefit of being able to assess relationships
between environmental contamination and EED biomarkers, as well as
leveraging the multivisit data collection infrastructure that was
already in place for the cohort. The intensive assessment was comprised
of stored drinking water samples, child hand and sentinel plastic
toy ball (after ∼24 h of play) rinses, fly enumeration near
food preparation areas and latrines, and visible hand cleanliness
observations for mothers and children. Child hand and sentinel toy
rinse samples were collected as proxy indicators of the overall environmental
fecal contamination in the household and representatives of likely
exposure pathways for children under 2 years old. We also measured selected indicators of environmental contamination
in similar sized subsets of households enrolled in the single water,
sanitation, and handwashing arms . Specifically, stored drinking water samples
were collected from the water and handwashing arms, and fly densities
near food preparation areas and latrines were measured in the sanitation
arm. Stored drinking water was sampled in the handwashing arm because
previous evidence suggests that caregiver hand and stored water contamination
are highly correlated, and stored drinking water fecal bacterial levels
are typically less variable than hand rinse fecal bacterial levels. , , All environmental samples (stored water, child
hand rinses, sentinel
toy rinses) were collected according to previously published protocols,
analyzed by membrane filtration, and incubated on MI media to isolate
and enumerate E. coli . , Fly densities (counts) were measured at food preparation areas and
latrines using the scudder fly grill method; flies were classified
as house, bottle, flesh, or “other” species. Field staff
observed, as a binary measure, whether there was visible dirt on mothers’
and children’s hands (palms, fingerpads, underneath fingernails).
Additional sample collection and processing details are provided in Supporting Information S1 . Household animal
ownership data, including binary ownership and
number and type of animals owned, were recorded at the time of enrollment
and 2 years after intervention delivery . Data on child health outcomes (diarrhea,
linear growth) were also collected 2 years after intervention delivery.
Caregiver-reported diarrhea was defined as three or more watery stools
in 24 h or a single episode of blood in the stool within the past
7 days. Anthropometric measurements—including length, head
circumference, and weight—were taken by trained field workers
following standard protocols. , Reported child dates
of birth were verified, when available, against clinic cards, health
booklets, or other birth records (e.g., baptismal card). Z -scores were calculated for length-for-age using WHO child growth
standards; outliers were excluded following WHO recommendations. Statistical Analyses Intervention Effects on Environmental Contamination Considering that improved nutrition would not be expected to influence
environmental contamination, for models evaluating intervention impacts
and associations between household animal ownership and environmental
contamination, we grouped measurements collected from the WSH and
WSHN arms together (WSH/WSHN) as well as measurements collected from
the control and nutrition arms together (C/N). We published a prespecified
statistical analysis plan for evaluating WSH intervention impacts
on fecal contamination in household environments ( https://osf.io/eg2rc/ ). All intervention
impact statistical analyses were independently replicated by two different
authors (AJP, JMS). Outcomes measured in the single intervention arms
(water, sanitation, handwashing) and pooled combined arms (WSH/WSHN)
were compared to outcomes in the pooled control (C/N) arms. We estimated
unadjusted and adjusted intention-to-treat effects between arms, relying
on the unadjusted analysis as our primary analysis. We estimated log
reductions, prevalence ratios, and prevalence differences using generalized
linear models with robust standard errors, with cluster as the independent
unit. We used the modified Poisson regression for binary outcomes. , All models included fixed effects for randomization block (to take
advantage of the pair-matched design). In adjusted analyses, we used
targeted maximum likelihood estimation to adjust for prespecified
baseline covariates (e.g., the most recent time it rained for all
outcomes and how much children played with balls for toy rinses),
including only variables strongly associated with the outcome (a full
list of covariates is available in Supporting Information S1 ). We conducted
subgroup analyses by year of data collection (year 1 versus year 2). Associations between Animal Ownership and Environmental Contamination
and Child Health As post hoc analyses, we estimated unadjusted
and adjusted associations between household animal ownership and household
environmental contamination and child health outcomes, relying on
the analyses adjusted for relevant covariates (illustrated in directed
acyclic graphs for each outcome in Figures S2–S7 ) as our primary analyses. The measures of animal ownership for the
primary analyses were the log 10 -transformed number of animals
owned overall and by species (cattle, goats, sheep, poultry, dogs);
results from analyses using binary animal ownership are presented
in the Supporting Information . We evaluated
associations between cross-sectional (year 2) animal ownership and
environmental contamination outcomes ( E. coli in water, hand rinses, and toy rinses; flies at food preparation
areas) and acute diarrhea, for which recent child exposure to animal
feces was the hypothesized exposure pathway. We relied on a cross-sectional
rather than prospective approach for environmental contamination outcomes
due to high temporal variability in fecal indicator bacteria, which
are strongly dependent on environmental conditions. , However, we leveraged prospective (at the time of enrollment) animal
ownership data to assess associations with linear growth outcomes
(stunting; continuous length-for-age Z -scores). Although
the passive control arm was excluded from year 2 stool collection
activities due to budgeting reasons in the parent trial (during which
animal ownership and diarrhea data were collected), when assessing
associations between baseline animal ownership and child growth, data
from the passive control arm were still leveraged to increase statistical
power. Sheep ownership data was not collected at enrollment. Covariates,
including intervention arm, were prescreened for their association
with each outcome (likelihood ratio test p -value
<0.2) and variation (prevalence ≥5%). Nutritional intervention
arms were pooled as above (WSH/WSHN; C/N) for environmental contamination
outcomes, and all intervention arms were kept separate for child health
outcomes. We used generalized linear models with robust standard errors
to account for village-level clustering in the parent trial. We used
modified Poisson regressions with log links to estimate prevalence
ratios and linear probability models to estimate prevalence differences
for binary outcomes; we also used linear probability models to estimate
differences in log-transformed bacterial counts for E. coli concentration and fly counts for fly density.
For examining the association between animal ownership and E. coli prevalence and concentration in stored water,
we excluded households with detectable free chlorine in their stored
drinking water at year 2, as detectable chlorine in water samples
could mask the associations between animal ownership and water contamination.
A sensitivity analysis, which included households with detectable
free chlorine (adjusted for the presence of chlorine in stored water),
had similar results to excluding households with free chlorine in
their drinking water ( Figure S8 ). We conducted several sensitivity analyses as robustness checks for
the models examining associations between animal ownership and environmental
contamination and child health. To account for the larger number of
poultry owned by households compared to other species (cattle, goats,
sheep, dogs), we calculated Tropical Livestock Unit (TLU) scores,
which are commonly used weighted measures of domesticated animals. , We adapted the following weighting factors for different animal
species across sub-Saharan Africa based on their metabolic weights,
as recommended by Njuki et al. in 2011: 0.5 for cattle, 0.1 for sheep and goats, 0.02 for poultry (chickens,
duck, geese, turkey), and 0.1 for dogs. Similar to previous studies,
we created five categories of TLU scores to represent varying livestock
compositions across households as follows: (1) no animals (0 TLU),
(2) a few chickens (0.02–0.08 TLU), (3) chickens or dogs or
small ruminants (0.10–0.48), (4) 1–2 cattle or ≥10
poultry (0.50–1.48 TLU), and (5) ≥3 cattle (≥1.5
TLU). , When assessing binary animal ownership as
an exposure, we also excluded observations with extreme predicted
probabilities of animal ownership (within 1% of the minimum and maximum
predicted probabilities across all households). SuperLearner was used
to estimate the predicted probability of animal ownership for all
covariates considered and to discover and prevent positivity violations
(lack of variation in animal ownership in a specific covariate stratum). For the prospective analyses of linear growth
outcomes, we conducted three different sensitivity analyses: (1) excluding
households that changed animal ownership status between enrollment
and 2 years after intervention delivery, (2) reclassifying households
that obtained animals during the study as animal-owning households,
and (3) conducting subgroup analyses on households that went from
nonanimal-owning to animal-owning status and vice versa. As
a secondary analysis, we estimated unadjusted and adjusted associations
between all outcomes and community-level animal ownership (proxied
by the median number of animals owned by study households living in
the same village). 

The Kenya WASH Benefits trial enrolled
pregnant women in Kakamega, Bungoma, and Vihiga counties of rural
Western Kenya and followed the children born to these pregnancies
(including twins) for 2 years. Households in this region are often
organized into compounds, where multiple related families live together
in a defined area and share latrines and communal areas for cooking,
child play, and domestic animal grazing/housing. A random number generator
with reproducible seed was used to assign interventions to household
clusters; groups of nine geographically adjacent clusters were block-randomized
into a passive control arm, active control arm, or one of six intervention
arms (chlorinated drinking water; improved sanitation through pit
latrine upgrades with a reinforced slab and drop hole cover, child
potties, and scoops for removing human and animal feces from the homes
and yards of enrolled children; handwashing with soap; combined WSH
interventions; small quantity lipid-based nutrient supplementation;
combined WSH and nutritional (WSHN) interventions). Study design details
for the trial, including details regarding the passive control arm,
active control arm (including monthly visits by community health promoters),
six intervention arms, and behavior change messaging through monthly
visits for all intervention arms (including infant and young child
feeding counseling for the nutrition and WSHN arms) were previously
published ( Supporting Information S1 and Figure S1 ). , , 

Approximately
1 year and 2 years after intervention delivery (timed to match the
collection of the trial’s primary child health outcomes), we
assessed environmental contamination in a subset of approximately
1500 households (approximately 375 children from each of the control,
nutrition, combined WSH, and combined WSHN arms) that participated
in EED biomarker measurement and comprised the EED cohort of the trial
. The intensive
environmental contamination assessment was conducted in the EED subgroup
because of the added benefit of being able to assess relationships
between environmental contamination and EED biomarkers, as well as
leveraging the multivisit data collection infrastructure that was
already in place for the cohort. The intensive assessment was comprised
of stored drinking water samples, child hand and sentinel plastic
toy ball (after ∼24 h of play) rinses, fly enumeration near
food preparation areas and latrines, and visible hand cleanliness
observations for mothers and children. Child hand and sentinel toy
rinse samples were collected as proxy indicators of the overall environmental
fecal contamination in the household and representatives of likely
exposure pathways for children under 2 years old. We also measured selected indicators of environmental contamination
in similar sized subsets of households enrolled in the single water,
sanitation, and handwashing arms . Specifically, stored drinking water samples
were collected from the water and handwashing arms, and fly densities
near food preparation areas and latrines were measured in the sanitation
arm. Stored drinking water was sampled in the handwashing arm because
previous evidence suggests that caregiver hand and stored water contamination
are highly correlated, and stored drinking water fecal bacterial levels
are typically less variable than hand rinse fecal bacterial levels. , , All environmental samples (stored water, child
hand rinses, sentinel
toy rinses) were collected according to previously published protocols,
analyzed by membrane filtration, and incubated on MI media to isolate
and enumerate E. coli . , Fly densities (counts) were measured at food preparation areas and
latrines using the scudder fly grill method; flies were classified
as house, bottle, flesh, or “other” species. Field staff
observed, as a binary measure, whether there was visible dirt on mothers’
and children’s hands (palms, fingerpads, underneath fingernails).
Additional sample collection and processing details are provided in Supporting Information S1 . Household animal
ownership data, including binary ownership and
number and type of animals owned, were recorded at the time of enrollment
and 2 years after intervention delivery . Data on child health outcomes (diarrhea,
linear growth) were also collected 2 years after intervention delivery.
Caregiver-reported diarrhea was defined as three or more watery stools
in 24 h or a single episode of blood in the stool within the past
7 days. Anthropometric measurements—including length, head
circumference, and weight—were taken by trained field workers
following standard protocols. , Reported child dates
of birth were verified, when available, against clinic cards, health
booklets, or other birth records (e.g., baptismal card). Z -scores were calculated for length-for-age using WHO child growth
standards; outliers were excluded following WHO recommendations. 

Intervention Effects on Environmental Contamination Considering that improved nutrition would not be expected to influence
environmental contamination, for models evaluating intervention impacts
and associations between household animal ownership and environmental
contamination, we grouped measurements collected from the WSH and
WSHN arms together (WSH/WSHN) as well as measurements collected from
the control and nutrition arms together (C/N). We published a prespecified
statistical analysis plan for evaluating WSH intervention impacts
on fecal contamination in household environments ( https://osf.io/eg2rc/ ). All intervention
impact statistical analyses were independently replicated by two different
authors (AJP, JMS). Outcomes measured in the single intervention arms
(water, sanitation, handwashing) and pooled combined arms (WSH/WSHN)
were compared to outcomes in the pooled control (C/N) arms. We estimated
unadjusted and adjusted intention-to-treat effects between arms, relying
on the unadjusted analysis as our primary analysis. We estimated log
reductions, prevalence ratios, and prevalence differences using generalized
linear models with robust standard errors, with cluster as the independent
unit. We used the modified Poisson regression for binary outcomes. , All models included fixed effects for randomization block (to take
advantage of the pair-matched design). In adjusted analyses, we used
targeted maximum likelihood estimation to adjust for prespecified
baseline covariates (e.g., the most recent time it rained for all
outcomes and how much children played with balls for toy rinses),
including only variables strongly associated with the outcome (a full
list of covariates is available in Supporting Information S1 ). We conducted
subgroup analyses by year of data collection (year 1 versus year 2). Associations between Animal Ownership and Environmental Contamination
and Child Health As post hoc analyses, we estimated unadjusted
and adjusted associations between household animal ownership and household
environmental contamination and child health outcomes, relying on
the analyses adjusted for relevant covariates (illustrated in directed
acyclic graphs for each outcome in Figures S2–S7 ) as our primary analyses. The measures of animal ownership for the
primary analyses were the log 10 -transformed number of animals
owned overall and by species (cattle, goats, sheep, poultry, dogs);
results from analyses using binary animal ownership are presented
in the Supporting Information . We evaluated
associations between cross-sectional (year 2) animal ownership and
environmental contamination outcomes ( E. coli in water, hand rinses, and toy rinses; flies at food preparation
areas) and acute diarrhea, for which recent child exposure to animal
feces was the hypothesized exposure pathway. We relied on a cross-sectional
rather than prospective approach for environmental contamination outcomes
due to high temporal variability in fecal indicator bacteria, which
are strongly dependent on environmental conditions. , However, we leveraged prospective (at the time of enrollment) animal
ownership data to assess associations with linear growth outcomes
(stunting; continuous length-for-age Z -scores). Although
the passive control arm was excluded from year 2 stool collection
activities due to budgeting reasons in the parent trial (during which
animal ownership and diarrhea data were collected), when assessing
associations between baseline animal ownership and child growth, data
from the passive control arm were still leveraged to increase statistical
power. Sheep ownership data was not collected at enrollment. Covariates,
including intervention arm, were prescreened for their association
with each outcome (likelihood ratio test p -value
<0.2) and variation (prevalence ≥5%). Nutritional intervention
arms were pooled as above (WSH/WSHN; C/N) for environmental contamination
outcomes, and all intervention arms were kept separate for child health
outcomes. We used generalized linear models with robust standard errors
to account for village-level clustering in the parent trial. We used
modified Poisson regressions with log links to estimate prevalence
ratios and linear probability models to estimate prevalence differences
for binary outcomes; we also used linear probability models to estimate
differences in log-transformed bacterial counts for E. coli concentration and fly counts for fly density.
For examining the association between animal ownership and E. coli prevalence and concentration in stored water,
we excluded households with detectable free chlorine in their stored
drinking water at year 2, as detectable chlorine in water samples
could mask the associations between animal ownership and water contamination.
A sensitivity analysis, which included households with detectable
free chlorine (adjusted for the presence of chlorine in stored water),
had similar results to excluding households with free chlorine in
their drinking water ( Figure S8 ). We conducted several sensitivity analyses as robustness checks for
the models examining associations between animal ownership and environmental
contamination and child health. To account for the larger number of
poultry owned by households compared to other species (cattle, goats,
sheep, dogs), we calculated Tropical Livestock Unit (TLU) scores,
which are commonly used weighted measures of domesticated animals. , We adapted the following weighting factors for different animal
species across sub-Saharan Africa based on their metabolic weights,
as recommended by Njuki et al. in 2011: 0.5 for cattle, 0.1 for sheep and goats, 0.02 for poultry (chickens,
duck, geese, turkey), and 0.1 for dogs. Similar to previous studies,
we created five categories of TLU scores to represent varying livestock
compositions across households as follows: (1) no animals (0 TLU),
(2) a few chickens (0.02–0.08 TLU), (3) chickens or dogs or
small ruminants (0.10–0.48), (4) 1–2 cattle or ≥10
poultry (0.50–1.48 TLU), and (5) ≥3 cattle (≥1.5
TLU). , When assessing binary animal ownership as
an exposure, we also excluded observations with extreme predicted
probabilities of animal ownership (within 1% of the minimum and maximum
predicted probabilities across all households). SuperLearner was used
to estimate the predicted probability of animal ownership for all
covariates considered and to discover and prevent positivity violations
(lack of variation in animal ownership in a specific covariate stratum). For the prospective analyses of linear growth
outcomes, we conducted three different sensitivity analyses: (1) excluding
households that changed animal ownership status between enrollment
and 2 years after intervention delivery, (2) reclassifying households
that obtained animals during the study as animal-owning households,
and (3) conducting subgroup analyses on households that went from
nonanimal-owning to animal-owning status and vice versa. As
a secondary analysis, we estimated unadjusted and adjusted associations
between all outcomes and community-level animal ownership (proxied
by the median number of animals owned by study households living in
the same village). 

Considering that improved nutrition would not be expected to influence
environmental contamination, for models evaluating intervention impacts
and associations between household animal ownership and environmental
contamination, we grouped measurements collected from the WSH and
WSHN arms together (WSH/WSHN) as well as measurements collected from
the control and nutrition arms together (C/N). We published a prespecified
statistical analysis plan for evaluating WSH intervention impacts
on fecal contamination in household environments ( https://osf.io/eg2rc/ ). All intervention
impact statistical analyses were independently replicated by two different
authors (AJP, JMS). Outcomes measured in the single intervention arms
(water, sanitation, handwashing) and pooled combined arms (WSH/WSHN)
were compared to outcomes in the pooled control (C/N) arms. We estimated
unadjusted and adjusted intention-to-treat effects between arms, relying
on the unadjusted analysis as our primary analysis. We estimated log
reductions, prevalence ratios, and prevalence differences using generalized
linear models with robust standard errors, with cluster as the independent
unit. We used the modified Poisson regression for binary outcomes. , All models included fixed effects for randomization block (to take
advantage of the pair-matched design). In adjusted analyses, we used
targeted maximum likelihood estimation to adjust for prespecified
baseline covariates (e.g., the most recent time it rained for all
outcomes and how much children played with balls for toy rinses),
including only variables strongly associated with the outcome (a full
list of covariates is available in Supporting Information S1 ). We conducted
subgroup analyses by year of data collection (year 1 versus year 2). 

As post hoc analyses, we estimated unadjusted
and adjusted associations between household animal ownership and household
environmental contamination and child health outcomes, relying on
the analyses adjusted for relevant covariates (illustrated in directed
acyclic graphs for each outcome in Figures S2–S7 ) as our primary analyses. The measures of animal ownership for the
primary analyses were the log 10 -transformed number of animals
owned overall and by species (cattle, goats, sheep, poultry, dogs);
results from analyses using binary animal ownership are presented
in the Supporting Information . We evaluated
associations between cross-sectional (year 2) animal ownership and
environmental contamination outcomes ( E. coli in water, hand rinses, and toy rinses; flies at food preparation
areas) and acute diarrhea, for which recent child exposure to animal
feces was the hypothesized exposure pathway. We relied on a cross-sectional
rather than prospective approach for environmental contamination outcomes
due to high temporal variability in fecal indicator bacteria, which
are strongly dependent on environmental conditions. , However, we leveraged prospective (at the time of enrollment) animal
ownership data to assess associations with linear growth outcomes
(stunting; continuous length-for-age Z -scores). Although
the passive control arm was excluded from year 2 stool collection
activities due to budgeting reasons in the parent trial (during which
animal ownership and diarrhea data were collected), when assessing
associations between baseline animal ownership and child growth, data
from the passive control arm were still leveraged to increase statistical
power. Sheep ownership data was not collected at enrollment. Covariates,
including intervention arm, were prescreened for their association
with each outcome (likelihood ratio test p -value
<0.2) and variation (prevalence ≥5%). Nutritional intervention
arms were pooled as above (WSH/WSHN; C/N) for environmental contamination
outcomes, and all intervention arms were kept separate for child health
outcomes. We used generalized linear models with robust standard errors
to account for village-level clustering in the parent trial. We used
modified Poisson regressions with log links to estimate prevalence
ratios and linear probability models to estimate prevalence differences
for binary outcomes; we also used linear probability models to estimate
differences in log-transformed bacterial counts for E. coli concentration and fly counts for fly density.
For examining the association between animal ownership and E. coli prevalence and concentration in stored water,
we excluded households with detectable free chlorine in their stored
drinking water at year 2, as detectable chlorine in water samples
could mask the associations between animal ownership and water contamination.
A sensitivity analysis, which included households with detectable
free chlorine (adjusted for the presence of chlorine in stored water),
had similar results to excluding households with free chlorine in
their drinking water ( Figure S8 ). We conducted several sensitivity analyses as robustness checks for
the models examining associations between animal ownership and environmental
contamination and child health. To account for the larger number of
poultry owned by households compared to other species (cattle, goats,
sheep, dogs), we calculated Tropical Livestock Unit (TLU) scores,
which are commonly used weighted measures of domesticated animals. , We adapted the following weighting factors for different animal
species across sub-Saharan Africa based on their metabolic weights,
as recommended by Njuki et al. in 2011: 0.5 for cattle, 0.1 for sheep and goats, 0.02 for poultry (chickens,
duck, geese, turkey), and 0.1 for dogs. Similar to previous studies,
we created five categories of TLU scores to represent varying livestock
compositions across households as follows: (1) no animals (0 TLU),
(2) a few chickens (0.02–0.08 TLU), (3) chickens or dogs or
small ruminants (0.10–0.48), (4) 1–2 cattle or ≥10
poultry (0.50–1.48 TLU), and (5) ≥3 cattle (≥1.5
TLU). , When assessing binary animal ownership as
an exposure, we also excluded observations with extreme predicted
probabilities of animal ownership (within 1% of the minimum and maximum
predicted probabilities across all households). SuperLearner was used
to estimate the predicted probability of animal ownership for all
covariates considered and to discover and prevent positivity violations
(lack of variation in animal ownership in a specific covariate stratum). For the prospective analyses of linear growth
outcomes, we conducted three different sensitivity analyses: (1) excluding
households that changed animal ownership status between enrollment
and 2 years after intervention delivery, (2) reclassifying households
that obtained animals during the study as animal-owning households,
and (3) conducting subgroup analyses on households that went from
nonanimal-owning to animal-owning status and vice versa. As
a secondary analysis, we estimated unadjusted and adjusted associations
between all outcomes and community-level animal ownership (proxied
by the median number of animals owned by study households living in
the same village). 

illustrates
the number of measurements collected for each outcome by study arm
and year of measurement. Levels of Environmental Contamination in the Control Group When combining the 1- and 2-year assessments, 94% of stored drinking
water samples were contaminated with E. coli (mean: 1.48 log 10 colony forming units [CFU]/100
mL, standard deviation (SD): 1.43, 1.54), 90% of child hands were
contaminated with E. coli (mean: 1.74 log 10 CFU/100 mL, SD: 1.63, 1.85), and 73% of toys were contaminated
with E. coli (mean: 0.58 log 10 CFU/toy, SD: 0.51, 0.65) in the control group (C/N) ( Table S1 ). One quarter (26%) of caregivers had
visible dirt observed on their palms or fingerpads, and over half
(54%) had dirt observed underneath their fingernails ( Table S3 ). Approximately one-third (36%) of children
had visible dirt observed on their palms or fingerpads, while two-thirds
(67%) had dirt observed underneath their fingernails. Flies were present
at 62% of food preparation areas (mean: 3.4 flies, SD: 6.2) and at
67% of latrines (mean: 3.7 flies, SD: 5.5) ( Table S3 ). House flies were more common at food preparation areas
(60% prevalence) than latrines (30%); bottle flies were observed at
3% of food preparation areas but were observed at more than half of
latrines (55% prevalence); flesh flies were observed at <1% of
food preparation areas and 3% of latrines ( Table S4 ). Intervention Effects on Environmental Contamination We previously reported indicators of intervention uptake. In water intervention arms, the proportion of
households with detectable chlorine residual in their stored drinking
water ranged from 39 to 43% at year 1 and from 19 to 23% at year 2.
Among households in the sanitation intervention arms, the proportion
with access to an improved latrine was 89–90% at year 1 and
78–82% at year 2. In handwashing intervention arms, soap and
water were present at a handwashing location at 76–78% of households
at year 1 and at 19–23% at year 2. Intervention effects on
environmental contamination in stored water, on child hands, and on
sentinel toys, as well as on flies near latrines and food preparation
areas, are reported below. Water Quality Water treatment reduced the prevalence
of E. coli in drinking water by 34%
(prevalence ratio [PR]: 0.66, 95% confidence interval [CI] 0.58, 0.76)
after 1 year of intervention exposure and by 24% (PR: 0.76, 95% CI:
0.72, 0.81) 2 years after interventions began ( and Table S1 ).
The combined WSH intervention showed similar effects on water quality
to water treatment alone (45% reduction in E. coli at year 1; 19% reduction at year 2). Handwashing with soap slightly
reduced the prevalence of E. coli contamination
in stored drinking water at year 2 (PR: 0.95, 95% CI: 0.92, 0.99)
but not at year 1; the handwashing intervention tended to reduce E. coli concentrations in stored water, but results
were not significant (year 1 log 10 CFU/100 mL difference:
−0.05, year 1 95% CI: −0.22, 0.12; year 2 log 10 CFU/100 mL difference: −0.07, year 2 95% CI: −0.19,
0.05). Differences in log-transformed E. coli concentrations across all intervention groups are reported in Table S1 . Child Hand and Toy Contamination The combined WSH interventions
did not affect the presence or levels of fecal indicator bacteria
on child hands or toy balls at any time point ( and Table S1 ).
The combined WSH interventions reduced the prevalence of visible dirt
on caregiver hands (PR averaged over both measurements: 0.86, 95%
CI: 0.78, 0.95) and under caregiver fingernails (PR averaged over
both measurements: 0.90, 95% CI: 0.85, 0.96) but did not affect visible
dirt on child hands or underneath child fingernails ( Table S3 ). These data suggest that handwashing frequency marginally
increased in the WSH arms, but these increases were not sufficient
to reduce E. coli contamination on
hands. Fly Prevalence and Densities The combined WSH intervention
reduced the prevalence and density of flies near the latrine at year
1 (PR: 0.88, 95% CI: 0.79, 0.97; −0.6 less flies counted, 95%
CI: −1.2, 0.0) but not at year 2 ( and Table S3 ).
No interventions affected the prevalence or density of flies in the
food preparation area. We also did not detect any differences in the
prevalence of fly species between the study arms ( Table S4 ). Animal Ownership Animal ownership was high in our study
population, with 90.2% (3777/4187) and 90.6% (3796/4192) of households
owning any animal at enrollment and year 2, respectively. By
species, household poultry ownership was most common (enrollment,
year 2 : 85.3, 84.7), followed by cattle (47.2, 51.4), dog (20.0,
14.9), goat (12.5, 16.4), and sheep (not measured, 5.56) ownership.
Among animal-owning households, households owned an average of 9 animals
(SD: 9) at enrollment and year 2. By species at enrollment and year
2, animal-owning households owned the greatest number of poultry (mean
[SD]: 8 [8]), followed by cattle (2 [2]), goats (2 [2]), sheep (2
[2]), and dogs (2 [1]). Between enrollment and year 2, 15.1% (633/4192)
of households changed ownership status for any type of animal. The
greatest number of households changed cattle ownership status (30.4%),
followed by poultry (23.0%), dogs (20.8%), and goats (19.4%); sheep
ownership data was not collected at enrollment. Associations between Animal Ownership and Environmental Contamination Water Quality Associations between animal ownership
and E. coli prevalence and concentration
in stored water were assessed among households without detectable
free chlorine in their drinking water, as detectable chlorine in water
samples could mask these relationships. For every additional log 10 number of sheep owned by a household, the prevalence of E. coli in stored water was 10% greater (PR: 1.10,
95% CI: 1.04, 1.15) at year 2 . The prevalence of E. coli in water was also 6% (PR: 1.06, 95% CI: 1.02, 1.10) higher in sheep-owning
households than in households without sheep ( Figures S10 and S11 ). There were no other associations between household
animal ownership and drinking water quality. There were even stronger
associations between community-level sheep ownership and E. coli in water (PR: 1.16, 95% CI: 1.08, 1.26; 0.88 log 10 CFU/100 mL increase, 95% CI: 0.28–1.47 for every
additional log 10 median number of sheep owned in the same
village) ( Figure S12 ). Higher numbers of
goats, poultry, and any animal species owned in the community were
also associated with higher E. coli concentrations in water ( Figure S12 ).
Specifically, E. coli concentrations
in water were 0.52 (goats), 0.88 (poultry), and 0.35 (any animal species)
log 10 CFU/100 mL higher for every additional log 10 median number of animals owned in the community for each animal
category. Child Hand and Toy Contamination A 1 log 10 increase in the number of goats owned by a household was
associated with 7% (PR: 1.07, 95% CI: 1.01, 1.14) greater E. coli prevalence and 0.22 log 10 CFU/100 mL (difference: 0.22, 95% CI: 0.09, 2.29) higher E. coli concentration on child hands . Higher TLU scores were associated
with higher E. coli concentrations
on child hands, though the result was only significant for TLU scores
between 0.10 and 0.48 (log 10 CFU/100 mL difference: 0.24,
95% CI: 0.03, 0.45; Figure S9 ). Binary
household goat ownership was also associated with higher E. coli prevalence and concentration on hands (PR:
1.05, 95% CI: 1.01, 1.09; log 10 CFU/100 mL difference:
0.17, 95% CI: 0.04, 0.30; Figures S10 and S11 ). Sensitivity analyses revealed potential associations between poultry
and any animal ownership and higher E. coli concentration on hands, but there were no other associations between
household animal ownership and hand contamination ( Figures S10–S11 ). Community-level goat ownership showed
slightly stronger positive associations with E. coli prevalence and concentration on child hands (PR: 1.16, 95% CI: 1.08,
1.25; log 10 CFU/100 mL difference: 0.50, 95% CI: 0.04,
0.95) ( Figure S12 ). Household animal
ownership was not associated with E. coli prevalence or concentration on sentinel toys ( and S9–S11 ). An increase of 1 log 10 village-median number
of goats owned, however, was associated with a 34% higher prevalence
of E. coli in toy ball rinses (PR:
1.34, 95% CI: 1.00, 1.80) ( Figure S12 ). Fly Prevalence and Densities Increased numbers of ruminants
(cattle, goats, sheep) and any animal species owned by households
were associated with higher fly prevalence and densities (excluding
sheep ownership) at food preparation areas . Higher TLU scores trended toward increased
fly prevalence and densities, though the result was only significant
for scores ≥1.5 ( Figure S9 ). Similarly,
binary cattle and goat ownership were associated with 11–18%
greater fly prevalence and 0.69–1.28 more flies counted in
food preparation areas ( Figures S10 and S11 ). Community-level ownership of goats and any animal species was
also associated with greater fly prevalence and densities in household
food preparation areas (Figure S12). Associations between Animal Ownership and Child Health Diarrhea data were collected from 5795 children at year 2, and length-for-age
data were collected from 6587 children. Overall, neither household
nor community-level animal ownership of any species was associated
with the prevalence of diarrhea in young children ( , S13–S15, and S18 ). However, a sensitivity analysis revealed that binary
poultry ownership among households with extreme predicted probabilities
excluded could be associated with a lower prevalence of diarrhea ( Figure S15 ). Greater numbers of cattle, poultry,
or any animal species owned by a household were associated with 0.10–0.24
higher length-for-age Z -scores and consequently 15–33%
lower prevalence of stunting (PR: 0.67–0.85), among under-5
children .
TLU scores ≥0.50 were also associated with higher length-for-age Z -scores and lower prevalence of stunting ( Figure S13 ). The prevalence of stunting was 24% (PR: 0.76,
95% CI: 0.69, 0.85) lower in cattle-owning households compared to
that in noncattle-owning households ( Figures S14 and S15 ). Conversely, the prevalence of stunting was 20% (PR:
1.20, 95% CI: 1.03, 1.42) higher in households that changed from owning
any animals to not owning animals between enrollment and year 2 (compared
to staying the same status or changing to owning animals) ( Figure S17 ). Community-level ownership of any
animal species overall was associated with decreased stunting (PR:
0.83, 95% CI: 0.70–0.98) in young children ( Figure S18 ). Discussion We found limited impacts of WSH interventions,
alone and in combination, on E. coli contamination in rural Kenyan households. Specifically, water treatment
with chlorine reduced and handwashing marginally reduced the prevalence
and concentration of E. coli in stored
water, while none of the interventions impacted E.
coli prevalence or concentrations on child hands or
toy balls. Our findings confirm that drinking water chlorination is
an effective method to reduce chlorine-susceptible contaminants in
low-income settings. If adoption of chlorine
had been higher in the parent trial than the roughly 40% observed
at year 1 and 20% at year 2, the reductions in stored water E. coli contamination likely would have been larger. , The effectiveness of the water intervention in improving water quality
complements observed reductions in roundworm ( Ascaris
lumbricoides ) infection prevalence among children
receiving the water intervention in the trial. Ascaris infection prevalence was reduced
by 18–22% in the intervention arms that included a water treatment
component (water, WSH, and WSHN arms) but not in other intervention
arms. Further, water chlorination may be an important strategy for
protecting household members from zoonotic enteropathogens, as sheep
ownership was associated with increased E. coli contamination in drinking water. Similarly, previous work in the
same study area found that 89% (40/45) of household stored water and
27% (12/45) of source water samples were contaminated with the ruminant-specific
fecal marker, BacR. While most households
(>68%) enrolled in the WASH Benefits Kenya trial accessed protected
springs or wells as their primary drinking water source, roughly a
quarter of households relied on unprotected water sources (e.g., unprotected
springs, dug wells, surface water) that are prone to contamination
by animal feces. , Our findings are consistent with
previous work in rural India that found that higher village sheep
populations increased the odds of detecting higher Cryptosporidium spp. concentrations in ponds. Importantly,
many child diarrheal disease episodes are attributable to chlorine-resistant
organisms, including those carried by ruminant hosts (e.g., Cryptosporidium spp.). Chlorine
might therefore need to be incorporated along with other strategies
(e.g., filtration or source and distribution system improvements)
to comprehensively improve water quality in low-income settings. A previous study in Tanzania found that
despite heterogeneity in random hand rinse measurements, the level
of fecal indicator bacteria on caregiver hands was the strongest predictor
of the level of fecal indicator bacteria in stored drinking water. , , Our finding that the handwashing
intervention marginally reduced E. coli concentration in water supports a link between hand contamination
and stored drinking water quality; however, the magnitude of the improvement
in water quality was small. Further trials that achieve high rates
of handwashing with soap could be useful to better quantify the effect
of increased frequency of handwashing with soap on drinking water
quality. The lack of effect on fecal indicator bacteria measured
on hands
and toys has several possible explanations, including inconsistent
compliance with the targeted hygiene and sanitation behaviors, failure
of these specific types of WSH interventions to reduce human fecal
contamination on child hands and toys, or animal fecal contamination
in the household environment. , , In addition, E. coli may be a poor
proxy for fecal contamination, despite its frequent use as a fecal
indicator bacteria. Our findings are
consistent with the parallel WASH Benefits trial in Bangladesh, which
also reported no reduction in child hand or toy E.
coli contamination in the WSH intervention arms despite
having higher uptake of the interventions. Notably, our post hoc analyses revealed that child hand contamination
was higher in households owning greater numbers of goats. Similarly,
ruminant fecal markers were prevalent in child hand rinse samples
in urban and rural Bangladesh. , Hand contamination
can be an especially important risk factor for the transmission of
zoonotic pathogens to young children, as hand- and object-mouthing
is frequent in children from low-income settings. Flies are recognized vectors for human pathogens,
and fly control
programs have been found effective in reducing diarrheal illness. − We observed marginal fly reductions at latrines from the combined
WSH intervention and no fly reductions at food preparation areas.
A trial evaluating community-led total sanitation in Mali detected
a reduction in fly presence at latrines, while other trials in rural
India and The Gambia found no reduction. , , Fly reductions in this trial may have been
limited due to insufficient adherence to the sanitation intervention.
Although household access to a latrine with an enumerator-observed
slab or ventilation pipe remained higher than 78% among households
in sanitation arms, the reported safe disposal of child feces into
a latrine and observed placements of drop hole covers over latrines
declined over the duration of the parent trial. Notably, the intervention was delivered at the compound
rather than the community level (neighboring compounds to the study
compound without a pregnant woman did not receive upgraded pit latrines
with covers). The house fly ( Musca domestica), which
was the most prevalent species in our study site, has a flight range
of up to 7 km and could travel daily between households. Additionally, the scoop provided in the sanitation
intervention may have been insufficient for removing animal feces
from household living areas. We found that animal ownership, particularly
ruminant ownership, was associated with higher fly prevalence and
densities near food preparation areas. The WASH Benefits trial in
Bangladesh found that a higher concentration of E.
coli on flies caught in the food preparation area
was associated with greater E. coli contamination of food, though the presence of animal feces was not
associated with E. coli levels on flies. Notably, E. coli was only detected in 50% of the sampled flies. Fly presence and
densities are therefore imperfect proxies for fecal contamination
transferred by flies, and future work may want to assess whether WSH
interventions can reduce the prevalence and concentration of fecal
contaminants on flies, even when total fly populations are not impacted.
In this study, we found that village-level ownership of ruminants,
particularly sheep and goats, was associated with fly presence near
household food preparation areas, as well as E. coli contamination in household drinking water, on child hands, and on
sentinel toys; future interventions for interrupting community-wide
enteropathogen transmission could therefore be more effective than
intervening at the individual household level. Although animal
ownership was associated with increased fecal contamination—albeit
with relatively small differences in E. coli concentrations—in our study households, it did not appear
to be a risk factor for under-5 child diarrhea or growth faltering.
On the contrary, household cattle and poultry ownership were associated
with higher length-for-age Z -scores and lower prevalence
of stunting; the prevalence of stunting was also higher in households
that owned any animals at enrollment but not at year 2, compared to
households that did not change ownership status or obtained animals
during the study. Livestock husbandry is often promoted as a means
to improve livelihoods and incur nutritional benefits—through
increased consumption of animal-sourced food—for low-income
households. , Our work is consistent with a
growing body of evidence that owning domesticated animals may reduce
stunting among young children by increasing the consumption of nutrient-rich
animal-sourced foods, household income, and women’s empowerment. , , The true relationship between
animal exposure and linear growth is likely dependent on specific
animal husbandry practices within the household. In rural Ethiopia, for example, poultry ownership was associated
with increased length-for-age Z -scores, but corralling
poultry inside the home overnight was negatively associated with child
growth. As food animal production is
predicted to increase in LMICs and animal husbandry is often promoted
to improve livelihoods and nutrition in low-income households, key
behaviors affecting human exposure to animal fecal contamination under
varying contexts (e.g., rural versus urban; domestic versus community)
should be addressed in future research. , , Limitations There are some limitations in this study.
First, fecal indicator bacteria can be a poor proxy for enteric pathogens
and thus may exhibit limited associations with health outcomes in
individual studies. Future evaluations
should consider measuring specific human pathogens in water and in
the environment to better understand how WSH interventions affect
transmission pathways. Further, environmental sampling of E. coli cannot confirm whether E.
coli originated from fecal sources, as some strains
of E. coli can survive long time periods
and potentially reproduce in natural (i.e., extraintestinal) environments. Second, we have been careful to interpret the
intervention effects on environmental contamination alongside the
relatively limited adherence to the interventions in the parent trial.
Higher uptake of the interventions could have led to larger effects,
but our reported results may be relevant for large-scale WSH programs
that are unable to implement behavior change programs that are as
intensive as those in efficacy studies. Third, while we adjusted for
relevant covariates in the association between animal ownership and
other outcomes, including intervention arm, there is potential for
residual confounding in these analyses. Further, animal ownership
is likely correlated with some covariates, such as household asset
ownership as measures of socioeconomic status. We discovered and prevented
positivity violations by excluding households with extreme predictive
probabilities of animal ownership as sensitivity analyses for models
with binary animal ownership as the exposures. Implications Provision and promotion of chlorine for
water treatment improved drinking water quality, but adoption of the
water treatment intervention was much lower than expected by the end
of the study. This emphasizes the difficulty of achieving sustained
and consistent usage of household water treatment products with monthly,
or less frequent, behavior promotion visits. , We found no evidence that combining water, sanitation, and handwashing
interventions led to larger reductions in fecal contamination in the
household environment than single interventions, a finding consistent
with no additive benefit on health outcomes measured in this trial
or in other studies. , , − Our results indicate that the intensive WSH interventions, as implemented
in this study, did not reduce levels of fecal indicator bacteria on
child hands or toys, while they slightly reduced fly presence near
latrines and marginally improved visible hand cleanliness of caregivers.
The failure of the interventions to reduce fecal contamination along
important exposure pathways in the household suggests that WSH programs
that aim to improve child health may need to consider interventions
that cost more but also more comprehensively reduce fecal contamination
in the household setting; our findings provide additional support
for more effective WSH interventions. , Improved
animal feces management is one such consideration that has been called
for in transformative WSH solutions. There
are, however, two growing and opposing bodies of literature on the
risks and benefits of animal husbandry on health in LMICs. Our findings demonstrated that domesticated
animals were associated with E. coli prevalence and concentrations in the household environment but not
with under-5 child diarrhea or increased stunting in rural Kenya.
Our study therefore does not support restricting household animal
ownership to prevent child diarrheal disease or stunting and cautions
that future interventions may want to avoid promoting practices that
increase the cost or burden of household animal rearing, which might
jeopardize child growth benefits. 

When combining the 1- and 2-year assessments, 94% of stored drinking
water samples were contaminated with E. coli (mean: 1.48 log 10 colony forming units [CFU]/100
mL, standard deviation (SD): 1.43, 1.54), 90% of child hands were
contaminated with E. coli (mean: 1.74 log 10 CFU/100 mL, SD: 1.63, 1.85), and 73% of toys were contaminated
with E. coli (mean: 0.58 log 10 CFU/toy, SD: 0.51, 0.65) in the control group (C/N) ( Table S1 ). One quarter (26%) of caregivers had
visible dirt observed on their palms or fingerpads, and over half
(54%) had dirt observed underneath their fingernails ( Table S3 ). Approximately one-third (36%) of children
had visible dirt observed on their palms or fingerpads, while two-thirds
(67%) had dirt observed underneath their fingernails. Flies were present
at 62% of food preparation areas (mean: 3.4 flies, SD: 6.2) and at
67% of latrines (mean: 3.7 flies, SD: 5.5) ( Table S3 ). House flies were more common at food preparation areas
(60% prevalence) than latrines (30%); bottle flies were observed at
3% of food preparation areas but were observed at more than half of
latrines (55% prevalence); flesh flies were observed at <1% of
food preparation areas and 3% of latrines ( Table S4 ). 

We previously reported indicators of intervention uptake. In water intervention arms, the proportion of
households with detectable chlorine residual in their stored drinking
water ranged from 39 to 43% at year 1 and from 19 to 23% at year 2.
Among households in the sanitation intervention arms, the proportion
with access to an improved latrine was 89–90% at year 1 and
78–82% at year 2. In handwashing intervention arms, soap and
water were present at a handwashing location at 76–78% of households
at year 1 and at 19–23% at year 2. Intervention effects on
environmental contamination in stored water, on child hands, and on
sentinel toys, as well as on flies near latrines and food preparation
areas, are reported below. Water Quality Water treatment reduced the prevalence
of E. coli in drinking water by 34%
(prevalence ratio [PR]: 0.66, 95% confidence interval [CI] 0.58, 0.76)
after 1 year of intervention exposure and by 24% (PR: 0.76, 95% CI:
0.72, 0.81) 2 years after interventions began ( and Table S1 ).
The combined WSH intervention showed similar effects on water quality
to water treatment alone (45% reduction in E. coli at year 1; 19% reduction at year 2). Handwashing with soap slightly
reduced the prevalence of E. coli contamination
in stored drinking water at year 2 (PR: 0.95, 95% CI: 0.92, 0.99)
but not at year 1; the handwashing intervention tended to reduce E. coli concentrations in stored water, but results
were not significant (year 1 log 10 CFU/100 mL difference:
−0.05, year 1 95% CI: −0.22, 0.12; year 2 log 10 CFU/100 mL difference: −0.07, year 2 95% CI: −0.19,
0.05). Differences in log-transformed E. coli concentrations across all intervention groups are reported in Table S1 . Child Hand and Toy Contamination The combined WSH interventions
did not affect the presence or levels of fecal indicator bacteria
on child hands or toy balls at any time point ( and Table S1 ).
The combined WSH interventions reduced the prevalence of visible dirt
on caregiver hands (PR averaged over both measurements: 0.86, 95%
CI: 0.78, 0.95) and under caregiver fingernails (PR averaged over
both measurements: 0.90, 95% CI: 0.85, 0.96) but did not affect visible
dirt on child hands or underneath child fingernails ( Table S3 ). These data suggest that handwashing frequency marginally
increased in the WSH arms, but these increases were not sufficient
to reduce E. coli contamination on
hands. Fly Prevalence and Densities The combined WSH intervention
reduced the prevalence and density of flies near the latrine at year
1 (PR: 0.88, 95% CI: 0.79, 0.97; −0.6 less flies counted, 95%
CI: −1.2, 0.0) but not at year 2 ( and Table S3 ).
No interventions affected the prevalence or density of flies in the
food preparation area. We also did not detect any differences in the
prevalence of fly species between the study arms ( Table S4 ). 

Water treatment reduced the prevalence
of E. coli in drinking water by 34%
(prevalence ratio [PR]: 0.66, 95% confidence interval [CI] 0.58, 0.76)
after 1 year of intervention exposure and by 24% (PR: 0.76, 95% CI:
0.72, 0.81) 2 years after interventions began ( and Table S1 ).
The combined WSH intervention showed similar effects on water quality
to water treatment alone (45% reduction in E. coli at year 1; 19% reduction at year 2). Handwashing with soap slightly
reduced the prevalence of E. coli contamination
in stored drinking water at year 2 (PR: 0.95, 95% CI: 0.92, 0.99)
but not at year 1; the handwashing intervention tended to reduce E. coli concentrations in stored water, but results
were not significant (year 1 log 10 CFU/100 mL difference:
−0.05, year 1 95% CI: −0.22, 0.12; year 2 log 10 CFU/100 mL difference: −0.07, year 2 95% CI: −0.19,
0.05). Differences in log-transformed E. coli concentrations across all intervention groups are reported in Table S1 . 

The combined WSH interventions
did not affect the presence or levels of fecal indicator bacteria
on child hands or toy balls at any time point ( and Table S1 ).
The combined WSH interventions reduced the prevalence of visible dirt
on caregiver hands (PR averaged over both measurements: 0.86, 95%
CI: 0.78, 0.95) and under caregiver fingernails (PR averaged over
both measurements: 0.90, 95% CI: 0.85, 0.96) but did not affect visible
dirt on child hands or underneath child fingernails ( Table S3 ). These data suggest that handwashing frequency marginally
increased in the WSH arms, but these increases were not sufficient
to reduce E. coli contamination on
hands. 

The combined WSH intervention
reduced the prevalence and density of flies near the latrine at year
1 (PR: 0.88, 95% CI: 0.79, 0.97; −0.6 less flies counted, 95%
CI: −1.2, 0.0) but not at year 2 ( and Table S3 ).
No interventions affected the prevalence or density of flies in the
food preparation area. We also did not detect any differences in the
prevalence of fly species between the study arms ( Table S4 ). 

Animal ownership was high in our study
population, with 90.2% (3777/4187) and 90.6% (3796/4192) of households
owning any animal at enrollment and year 2, respectively. By
species, household poultry ownership was most common (enrollment,
year 2 : 85.3, 84.7), followed by cattle (47.2, 51.4), dog (20.0,
14.9), goat (12.5, 16.4), and sheep (not measured, 5.56) ownership.
Among animal-owning households, households owned an average of 9 animals
(SD: 9) at enrollment and year 2. By species at enrollment and year
2, animal-owning households owned the greatest number of poultry (mean
[SD]: 8 [8]), followed by cattle (2 [2]), goats (2 [2]), sheep (2
[2]), and dogs (2 [1]). Between enrollment and year 2, 15.1% (633/4192)
of households changed ownership status for any type of animal. The
greatest number of households changed cattle ownership status (30.4%),
followed by poultry (23.0%), dogs (20.8%), and goats (19.4%); sheep
ownership data was not collected at enrollment. 

Water Quality Associations between animal ownership
and E. coli prevalence and concentration
in stored water were assessed among households without detectable
free chlorine in their drinking water, as detectable chlorine in water
samples could mask these relationships. For every additional log 10 number of sheep owned by a household, the prevalence of E. coli in stored water was 10% greater (PR: 1.10,
95% CI: 1.04, 1.15) at year 2 . The prevalence of E. coli in water was also 6% (PR: 1.06, 95% CI: 1.02, 1.10) higher in sheep-owning
households than in households without sheep ( Figures S10 and S11 ). There were no other associations between household
animal ownership and drinking water quality. There were even stronger
associations between community-level sheep ownership and E. coli in water (PR: 1.16, 95% CI: 1.08, 1.26; 0.88 log 10 CFU/100 mL increase, 95% CI: 0.28–1.47 for every
additional log 10 median number of sheep owned in the same
village) ( Figure S12 ). Higher numbers of
goats, poultry, and any animal species owned in the community were
also associated with higher E. coli concentrations in water ( Figure S12 ).
Specifically, E. coli concentrations
in water were 0.52 (goats), 0.88 (poultry), and 0.35 (any animal species)
log 10 CFU/100 mL higher for every additional log 10 median number of animals owned in the community for each animal
category. Child Hand and Toy Contamination A 1 log 10 increase in the number of goats owned by a household was
associated with 7% (PR: 1.07, 95% CI: 1.01, 1.14) greater E. coli prevalence and 0.22 log 10 CFU/100 mL (difference: 0.22, 95% CI: 0.09, 2.29) higher E. coli concentration on child hands . Higher TLU scores were associated
with higher E. coli concentrations
on child hands, though the result was only significant for TLU scores
between 0.10 and 0.48 (log 10 CFU/100 mL difference: 0.24,
95% CI: 0.03, 0.45; Figure S9 ). Binary
household goat ownership was also associated with higher E. coli prevalence and concentration on hands (PR:
1.05, 95% CI: 1.01, 1.09; log 10 CFU/100 mL difference:
0.17, 95% CI: 0.04, 0.30; Figures S10 and S11 ). Sensitivity analyses revealed potential associations between poultry
and any animal ownership and higher E. coli concentration on hands, but there were no other associations between
household animal ownership and hand contamination ( Figures S10–S11 ). Community-level goat ownership showed
slightly stronger positive associations with E. coli prevalence and concentration on child hands (PR: 1.16, 95% CI: 1.08,
1.25; log 10 CFU/100 mL difference: 0.50, 95% CI: 0.04,
0.95) ( Figure S12 ). Household animal
ownership was not associated with E. coli prevalence or concentration on sentinel toys ( and S9–S11 ). An increase of 1 log 10 village-median number
of goats owned, however, was associated with a 34% higher prevalence
of E. coli in toy ball rinses (PR:
1.34, 95% CI: 1.00, 1.80) ( Figure S12 ). Fly Prevalence and Densities Increased numbers of ruminants
(cattle, goats, sheep) and any animal species owned by households
were associated with higher fly prevalence and densities (excluding
sheep ownership) at food preparation areas . Higher TLU scores trended toward increased
fly prevalence and densities, though the result was only significant
for scores ≥1.5 ( Figure S9 ). Similarly,
binary cattle and goat ownership were associated with 11–18%
greater fly prevalence and 0.69–1.28 more flies counted in
food preparation areas ( Figures S10 and S11 ). Community-level ownership of goats and any animal species was
also associated with greater fly prevalence and densities in household
food preparation areas (Figure S12). 

Associations between animal ownership
and E. coli prevalence and concentration
in stored water were assessed among households without detectable
free chlorine in their drinking water, as detectable chlorine in water
samples could mask these relationships. For every additional log 10 number of sheep owned by a household, the prevalence of E. coli in stored water was 10% greater (PR: 1.10,
95% CI: 1.04, 1.15) at year 2 . The prevalence of E. coli in water was also 6% (PR: 1.06, 95% CI: 1.02, 1.10) higher in sheep-owning
households than in households without sheep ( Figures S10 and S11 ). There were no other associations between household
animal ownership and drinking water quality. There were even stronger
associations between community-level sheep ownership and E. coli in water (PR: 1.16, 95% CI: 1.08, 1.26; 0.88 log 10 CFU/100 mL increase, 95% CI: 0.28–1.47 for every
additional log 10 median number of sheep owned in the same
village) ( Figure S12 ). Higher numbers of
goats, poultry, and any animal species owned in the community were
also associated with higher E. coli concentrations in water ( Figure S12 ).
Specifically, E. coli concentrations
in water were 0.52 (goats), 0.88 (poultry), and 0.35 (any animal species)
log 10 CFU/100 mL higher for every additional log 10 median number of animals owned in the community for each animal
category. 

A 1 log 10 increase in the number of goats owned by a household was
associated with 7% (PR: 1.07, 95% CI: 1.01, 1.14) greater E. coli prevalence and 0.22 log 10 CFU/100 mL (difference: 0.22, 95% CI: 0.09, 2.29) higher E. coli concentration on child hands . Higher TLU scores were associated
with higher E. coli concentrations
on child hands, though the result was only significant for TLU scores
between 0.10 and 0.48 (log 10 CFU/100 mL difference: 0.24,
95% CI: 0.03, 0.45; Figure S9 ). Binary
household goat ownership was also associated with higher E. coli prevalence and concentration on hands (PR:
1.05, 95% CI: 1.01, 1.09; log 10 CFU/100 mL difference:
0.17, 95% CI: 0.04, 0.30; Figures S10 and S11 ). Sensitivity analyses revealed potential associations between poultry
and any animal ownership and higher E. coli concentration on hands, but there were no other associations between
household animal ownership and hand contamination ( Figures S10–S11 ). Community-level goat ownership showed
slightly stronger positive associations with E. coli prevalence and concentration on child hands (PR: 1.16, 95% CI: 1.08,
1.25; log 10 CFU/100 mL difference: 0.50, 95% CI: 0.04,
0.95) ( Figure S12 ). Household animal
ownership was not associated with E. coli prevalence or concentration on sentinel toys ( and S9–S11 ). An increase of 1 log 10 village-median number
of goats owned, however, was associated with a 34% higher prevalence
of E. coli in toy ball rinses (PR:
1.34, 95% CI: 1.00, 1.80) ( Figure S12 ). 

Increased numbers of ruminants
(cattle, goats, sheep) and any animal species owned by households
were associated with higher fly prevalence and densities (excluding
sheep ownership) at food preparation areas . Higher TLU scores trended toward increased
fly prevalence and densities, though the result was only significant
for scores ≥1.5 ( Figure S9 ). Similarly,
binary cattle and goat ownership were associated with 11–18%
greater fly prevalence and 0.69–1.28 more flies counted in
food preparation areas ( Figures S10 and S11 ). Community-level ownership of goats and any animal species was
also associated with greater fly prevalence and densities in household
food preparation areas (Figure S12). 

Diarrhea data were collected from 5795 children at year 2, and length-for-age
data were collected from 6587 children. Overall, neither household
nor community-level animal ownership of any species was associated
with the prevalence of diarrhea in young children ( , S13–S15, and S18 ). However, a sensitivity analysis revealed that binary
poultry ownership among households with extreme predicted probabilities
excluded could be associated with a lower prevalence of diarrhea ( Figure S15 ). Greater numbers of cattle, poultry,
or any animal species owned by a household were associated with 0.10–0.24
higher length-for-age Z -scores and consequently 15–33%
lower prevalence of stunting (PR: 0.67–0.85), among under-5
children .
TLU scores ≥0.50 were also associated with higher length-for-age Z -scores and lower prevalence of stunting ( Figure S13 ). The prevalence of stunting was 24% (PR: 0.76,
95% CI: 0.69, 0.85) lower in cattle-owning households compared to
that in noncattle-owning households ( Figures S14 and S15 ). Conversely, the prevalence of stunting was 20% (PR:
1.20, 95% CI: 1.03, 1.42) higher in households that changed from owning
any animals to not owning animals between enrollment and year 2 (compared
to staying the same status or changing to owning animals) ( Figure S17 ). Community-level ownership of any
animal species overall was associated with decreased stunting (PR:
0.83, 95% CI: 0.70–0.98) in young children ( Figure S18 ). 

We found limited impacts of WSH interventions,
alone and in combination, on E. coli contamination in rural Kenyan households. Specifically, water treatment
with chlorine reduced and handwashing marginally reduced the prevalence
and concentration of E. coli in stored
water, while none of the interventions impacted E.
coli prevalence or concentrations on child hands or
toy balls. Our findings confirm that drinking water chlorination is
an effective method to reduce chlorine-susceptible contaminants in
low-income settings. If adoption of chlorine
had been higher in the parent trial than the roughly 40% observed
at year 1 and 20% at year 2, the reductions in stored water E. coli contamination likely would have been larger. , The effectiveness of the water intervention in improving water quality
complements observed reductions in roundworm ( Ascaris
lumbricoides ) infection prevalence among children
receiving the water intervention in the trial. Ascaris infection prevalence was reduced
by 18–22% in the intervention arms that included a water treatment
component (water, WSH, and WSHN arms) but not in other intervention
arms. Further, water chlorination may be an important strategy for
protecting household members from zoonotic enteropathogens, as sheep
ownership was associated with increased E. coli contamination in drinking water. Similarly, previous work in the
same study area found that 89% (40/45) of household stored water and
27% (12/45) of source water samples were contaminated with the ruminant-specific
fecal marker, BacR. While most households
(>68%) enrolled in the WASH Benefits Kenya trial accessed protected
springs or wells as their primary drinking water source, roughly a
quarter of households relied on unprotected water sources (e.g., unprotected
springs, dug wells, surface water) that are prone to contamination
by animal feces. , Our findings are consistent with
previous work in rural India that found that higher village sheep
populations increased the odds of detecting higher Cryptosporidium spp. concentrations in ponds. Importantly,
many child diarrheal disease episodes are attributable to chlorine-resistant
organisms, including those carried by ruminant hosts (e.g., Cryptosporidium spp.). Chlorine
might therefore need to be incorporated along with other strategies
(e.g., filtration or source and distribution system improvements)
to comprehensively improve water quality in low-income settings. A previous study in Tanzania found that
despite heterogeneity in random hand rinse measurements, the level
of fecal indicator bacteria on caregiver hands was the strongest predictor
of the level of fecal indicator bacteria in stored drinking water. , , Our finding that the handwashing
intervention marginally reduced E. coli concentration in water supports a link between hand contamination
and stored drinking water quality; however, the magnitude of the improvement
in water quality was small. Further trials that achieve high rates
of handwashing with soap could be useful to better quantify the effect
of increased frequency of handwashing with soap on drinking water
quality. The lack of effect on fecal indicator bacteria measured
on hands
and toys has several possible explanations, including inconsistent
compliance with the targeted hygiene and sanitation behaviors, failure
of these specific types of WSH interventions to reduce human fecal
contamination on child hands and toys, or animal fecal contamination
in the household environment. , , In addition, E. coli may be a poor
proxy for fecal contamination, despite its frequent use as a fecal
indicator bacteria. Our findings are
consistent with the parallel WASH Benefits trial in Bangladesh, which
also reported no reduction in child hand or toy E.
coli contamination in the WSH intervention arms despite
having higher uptake of the interventions. Notably, our post hoc analyses revealed that child hand contamination
was higher in households owning greater numbers of goats. Similarly,
ruminant fecal markers were prevalent in child hand rinse samples
in urban and rural Bangladesh. , Hand contamination
can be an especially important risk factor for the transmission of
zoonotic pathogens to young children, as hand- and object-mouthing
is frequent in children from low-income settings. Flies are recognized vectors for human pathogens,
and fly control
programs have been found effective in reducing diarrheal illness. − We observed marginal fly reductions at latrines from the combined
WSH intervention and no fly reductions at food preparation areas.
A trial evaluating community-led total sanitation in Mali detected
a reduction in fly presence at latrines, while other trials in rural
India and The Gambia found no reduction. , , Fly reductions in this trial may have been
limited due to insufficient adherence to the sanitation intervention.
Although household access to a latrine with an enumerator-observed
slab or ventilation pipe remained higher than 78% among households
in sanitation arms, the reported safe disposal of child feces into
a latrine and observed placements of drop hole covers over latrines
declined over the duration of the parent trial. Notably, the intervention was delivered at the compound
rather than the community level (neighboring compounds to the study
compound without a pregnant woman did not receive upgraded pit latrines
with covers). The house fly ( Musca domestica), which
was the most prevalent species in our study site, has a flight range
of up to 7 km and could travel daily between households. Additionally, the scoop provided in the sanitation
intervention may have been insufficient for removing animal feces
from household living areas. We found that animal ownership, particularly
ruminant ownership, was associated with higher fly prevalence and
densities near food preparation areas. The WASH Benefits trial in
Bangladesh found that a higher concentration of E.
coli on flies caught in the food preparation area
was associated with greater E. coli contamination of food, though the presence of animal feces was not
associated with E. coli levels on flies. Notably, E. coli was only detected in 50% of the sampled flies. Fly presence and
densities are therefore imperfect proxies for fecal contamination
transferred by flies, and future work may want to assess whether WSH
interventions can reduce the prevalence and concentration of fecal
contaminants on flies, even when total fly populations are not impacted.
In this study, we found that village-level ownership of ruminants,
particularly sheep and goats, was associated with fly presence near
household food preparation areas, as well as E. coli contamination in household drinking water, on child hands, and on
sentinel toys; future interventions for interrupting community-wide
enteropathogen transmission could therefore be more effective than
intervening at the individual household level. Although animal
ownership was associated with increased fecal contamination—albeit
with relatively small differences in E. coli concentrations—in our study households, it did not appear
to be a risk factor for under-5 child diarrhea or growth faltering.
On the contrary, household cattle and poultry ownership were associated
with higher length-for-age Z -scores and lower prevalence
of stunting; the prevalence of stunting was also higher in households
that owned any animals at enrollment but not at year 2, compared to
households that did not change ownership status or obtained animals
during the study. Livestock husbandry is often promoted as a means
to improve livelihoods and incur nutritional benefits—through
increased consumption of animal-sourced food—for low-income
households. , Our work is consistent with a
growing body of evidence that owning domesticated animals may reduce
stunting among young children by increasing the consumption of nutrient-rich
animal-sourced foods, household income, and women’s empowerment. , , The true relationship between
animal exposure and linear growth is likely dependent on specific
animal husbandry practices within the household. In rural Ethiopia, for example, poultry ownership was associated
with increased length-for-age Z -scores, but corralling
poultry inside the home overnight was negatively associated with child
growth. As food animal production is
predicted to increase in LMICs and animal husbandry is often promoted
to improve livelihoods and nutrition in low-income households, key
behaviors affecting human exposure to animal fecal contamination under
varying contexts (e.g., rural versus urban; domestic versus community)
should be addressed in future research. , , 

There are some limitations in this study.
First, fecal indicator bacteria can be a poor proxy for enteric pathogens
and thus may exhibit limited associations with health outcomes in
individual studies. Future evaluations
should consider measuring specific human pathogens in water and in
the environment to better understand how WSH interventions affect
transmission pathways. Further, environmental sampling of E. coli cannot confirm whether E.
coli originated from fecal sources, as some strains
of E. coli can survive long time periods
and potentially reproduce in natural (i.e., extraintestinal) environments. Second, we have been careful to interpret the
intervention effects on environmental contamination alongside the
relatively limited adherence to the interventions in the parent trial.
Higher uptake of the interventions could have led to larger effects,
but our reported results may be relevant for large-scale WSH programs
that are unable to implement behavior change programs that are as
intensive as those in efficacy studies. Third, while we adjusted for
relevant covariates in the association between animal ownership and
other outcomes, including intervention arm, there is potential for
residual confounding in these analyses. Further, animal ownership
is likely correlated with some covariates, such as household asset
ownership as measures of socioeconomic status. We discovered and prevented
positivity violations by excluding households with extreme predictive
probabilities of animal ownership as sensitivity analyses for models
with binary animal ownership as the exposures. 

Provision and promotion of chlorine for
water treatment improved drinking water quality, but adoption of the
water treatment intervention was much lower than expected by the end
of the study. This emphasizes the difficulty of achieving sustained
and consistent usage of household water treatment products with monthly,
or less frequent, behavior promotion visits. , We found no evidence that combining water, sanitation, and handwashing
interventions led to larger reductions in fecal contamination in the
household environment than single interventions, a finding consistent
with no additive benefit on health outcomes measured in this trial
or in other studies. , , − Our results indicate that the intensive WSH interventions, as implemented
in this study, did not reduce levels of fecal indicator bacteria on
child hands or toys, while they slightly reduced fly presence near
latrines and marginally improved visible hand cleanliness of caregivers.
The failure of the interventions to reduce fecal contamination along
important exposure pathways in the household suggests that WSH programs
that aim to improve child health may need to consider interventions
that cost more but also more comprehensively reduce fecal contamination
in the household setting; our findings provide additional support
for more effective WSH interventions. , Improved
animal feces management is one such consideration that has been called
for in transformative WSH solutions. There
are, however, two growing and opposing bodies of literature on the
risks and benefits of animal husbandry on health in LMICs. Our findings demonstrated that domesticated
animals were associated with E. coli prevalence and concentrations in the household environment but not
with under-5 child diarrhea or increased stunting in rural Kenya.
Our study therefore does not support restricting household animal
ownership to prevent child diarrheal disease or stunting and cautions
that future interventions may want to avoid promoting practices that
increase the cost or burden of household animal rearing, which might
jeopardize child growth benefits. 

