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Burden of disease from inadequate water, sanitation and hygiene for selected adverse health outcomes: An updated analysis with a focus on low- and middle-income countries

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Background: To develop updated estimates in response to new exposure and exposure-response data of the burden of diarrhoea, respiratory infections, malnutrition, schistosomiasis, malaria, soil-transmitted helminth infections and trachoma from exposure to inadequate drinking-water, sanitation and hygiene behaviours (WASH) with a focus on low- and middle-income countries. Methods: For each of the analysed diseases, exposure levels with both sufficient global exposure data for 2016 and a matching exposure-response relationship were combined into population-attributable fractions. Attributable deaths and disability-adjusted life years (DALYs) were estimated for each disease and, for most of the diseases, by country, age and sex group separately for inadequate water, sanitation and hygiene behaviours and for the cluster of risk factors. Uncertainty estimates were computed on the basis of uncertainty surrounding exposure estimates and relative risks. Findings: An estimated 829,000 WASH-attributable deaths and 49.8 million DALYs occurred from diarrhoeal diseases in 2016, equivalent to 60% of all diarrhoeal deaths. In children under 5 years, 297,000 WASH-attributable diarrhoea deaths occurred, representing 5.3% of all deaths in this age group. If the global disease burden from different diseases and several counterfactual exposure distributions was combined it would amount to 1.6 million deaths, representing 2.8% of all deaths, and 104.6 million DALYs in 2016. Conclusions: Despite recent declines in attributable mortality, inadequate WASH remains an important determinant of global disease burden, especially among young children. These estimates contribute to global monitoring such as for the Sustainable Development Goal indicator on mortality from inadequate WASH.
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International Journal of Hygiene and
Environmental Health
journal homepage: www.elsevier.com/locate/ijheh
Burden of disease from inadequate water, sanitation and hygiene for
selected adverse health outcomes: An updated analysis with a focus on low-
and middle-income countries
Annette Prüss-Ustün
a,
, Jennyfer Wolf
a
, Jamie Bartram
b
, Thomas Clasen
c
, Oliver Cumming
d
,
Matthew C. Freeman
c
, Bruce Gordon
a
, Paul R. Hunter
e,f
, Kate Medlicott
a
, Richard Johnston
a
a
Department of Public Health, Environment and Social Determinants of Health, World Health Organization, Geneva, Switzerland
b
Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
c
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
d
Department of Disease Control, London School of Hygiene and Tropical Medicine, London, UK
e
The Norwich School of Medicine, University of East Anglia, Norwich, UK
f
Department of Environmental Health, Tshwane University of Technology, Pretoria, South Africa
ARTICLE INFO
Keywords:
Burden of disease
Comparative risk assessment
Drinking water
Water
Sanitation
Hygiene
Diarrhoea
Hand washing
ABSTRACT
Background: To develop updated estimates in response to new exposure and exposure-response data of the
burden of diarrhoea, respiratory infections, malnutrition, schistosomiasis, malaria, soil-transmitted helminth
infections and trachoma from exposure to inadequate drinking-water, sanitation and hygiene behaviours
(WASH) with a focus on low- and middle-income countries.
Methods: For each of the analysed diseases, exposure levels with both sufficient global exposure data for 2016
and a matching exposure-response relationship were combined into population-attributable fractions.
Attributable deaths and disability-adjusted life years (DALYs) were estimated for each disease and, for most of
the diseases, by country, age and sex group separately for inadequate water, sanitation and hygiene behaviours
and for the cluster of risk factors. Uncertainty estimates were computed on the basis of uncertainty surrounding
exposure estimates and relative risks.
Findings: An estimated 829,000 WASH-attributable deaths and 49.8 million DALYs occurred from diarrhoeal
diseases in 2016, equivalent to 60% of all diarrhoeal deaths. In children under 5 years, 297,000 WASH-attri-
butable diarrhoea deaths occurred, representing 5.3% of all deaths in this age group. If the global disease burden
from different diseases and several counterfactual exposure distributions was combined it would amount to 1.6
million deaths, representing 2.8% of all deaths, and 104.6 million DALYs in 2016.
Conclusions: Despite recent declines in attributable mortality, inadequate WASH remains an important de-
terminant of global disease burden, especially among young children. These estimates contribute to global
monitoring such as for the Sustainable Development Goal indicator on mortality from inadequate WASH.
1. Introduction
Global burden of disease assessments are important to identify
priorities for improving population health and tracking changes in the
relative importance of different diseases, injuries and risk factors
(Murray and Lopez, 2013). The burden of disease from inadequate
drinking water, sanitation and hygiene behaviours (WASH) has been
estimated at various times in previous decades (Forouzanfar et al.,
2016,2015;Gakidou et al., 2017;Lim et al., 2012;Murray and Lopez,
1996;Prüss-Ustün et al., 2014,2008;Stanaway et al., 2018;WHO,
2004,2002); inadequate drinking water as used in this work includes
unsafe water and water with insufficient access. While some of these
https://doi.org/10.1016/j.ijheh.2019.05.004
Received 21 December 2018; Received in revised form 3 May 2019; Accepted 3 May 2019
Abbreviations: CRA, comparative risk assessment; DALYs, disability-adjusted life years; HICs, high-income countries; JMP, WHO/UNICEF Joint Monitoring
Programme for Water Supply, Sanitation and Hygiene; LMICs, low- and middle-income countries; WASH, water, sanitation and hygiene behaviours
Corresponding author. Department of Public Health, Environment and Social Determinants of Health, World Health Organization, 20 Avenue Appia, Geneva,
Switzerland.
E-mail addresses: pruessa@who.int (A. Prüss-Ustün), jennyfer.wolf@gmail.com,wolfj@who.int (J. Wolf), jbartram@email.unc.edu (J. Bartram),
thomas.f.clasen@emory.edu (T. Clasen), Oliver.Cumming@lshtm.ac.uk (O. Cumming), matthew.freeman@emory.edu (M.C. Freeman),
gordonb@who.int (B. Gordon), paul.hunter@uea.ac.uk (P.R. Hunter), medlicottk@who.int (K. Medlicott), johnstonr@who.int (R. Johnston).
International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
1438-4639/ © 2019 Published by Elsevier GmbH. This is an open access article under the CC BY 3.0 license (http://creativecommons.org/licenses/BY 3.0/IGO/).
Please cite this article as: Annette Prüss-Ustün, et al., International Journal of Hygiene and Environmental Health,
https://doi.org/10.1016/j.ijheh.2019.05.004
assessments focused on diarrhoeal disease (Forouzanfar et al., 2015;
Lim et al., 2012;Murray and Lopez, 1996;Prüss-Ustün et al., 2014;
WHO, 2002) others also assessed the WASH-attributable disease burden
of other health outcomes such as soil-transmitted helminth infections,
malaria, trachoma, schistosomiasis, lymphatic filariasis, lower re-
spiratory infections, and protein energy malnutrition (Forouzanfar
et al., 2016;Gakidou et al., 2017;Prüss-Ustün et al., 2008;Stanaway
et al., 2018;WHO, 2004). These assessments present very different
burden of disease estimates because of differences in methods used,
scope of the estimates, and ongoing improvements in WASH in many
regions (Clasen et al., 2014).
Despite improvements, inadequate WASH remains a major global
risk factor: In 2015, 844 million people lacked a basic drinking water
service, i.e., a drinking water source protected from recontamination
within 30 min’ round-trip to collect water, and nearly 30% of the global
population did not use a safely managed drinking water service—a
drinking water source located on premises, available when needed and
free from contamination (WHO and UNICEF, 2017). In terms of access
to sanitation, 2.3 billion people were lacking a basic sanitation servi-
ce—an improved sanitation facility that is not shared with other
households—and more than 60% were not using a safely managed sa-
nitation service—a sanitation facility that safely disposes excreta in-situ
or that ensures that excreta are safely treated off-site (WHO and
UNICEF, 2017). Estimates suggest that one in four persons worldwide
does not have access to a handwashing facility with soap and water on
premises and that only 26% of potential faecal contacts are followed by
handwashing with soap (Wolf et al., 2018b). Furthermore, only 45% of
the population live in communities in which coverage with basic sa-
nitation services is above 75% (Wolf et al., 2018c).
The objective of this paper is to present updated WASH-attributable
burden of diarrhoeal disease estimates for the year 2016 and to add the
WASH-attributable burden of further selected adverse health outcomes
including respiratory infections, malnutrition, schistosomiasis, malaria,
soil-transmitted helminth infections and trachoma. It needs to be ac-
knowledged that – depending on the available evidence - not all estimates
are based on the same level of evidence, use different counterfactual ex-
posure distributions and apply different assumptions. To reduce this dis-
ease burden from a broad range of diseases, very different intervention
strategies would be required which are further outlined below. This paper
provides the basis for reporting on Sustainable Development Goal in-
dicator (3.9.2) on WASH-attributable mortality (United Nations, 2018).
2. Methods
2.1. Framework for estimation
“Inadequate WASH” as used in this article spans a range of WASH
services, behaviours and related risks for specific health outcomes, in-
cluding, amongst others, drinking water, sanitation and hygiene (e.g.,
diarrhoea, protein-energy malnutrition), and water resources manage-
ment (e.g., malaria). Sanitation and drinking water services, and pre-
sence of a handwashing facility with soap and water on premises are
defined following the WHO/UNICEF Joint Monitoring Programme for
Water Supply, Sanitation and Hygiene (JMP)(WHO and UNICEF, un-
dated). Table 1 presents a list of adverse health outcomes that can at
least partly be attributed to inadequate WASH and whether this relation
has previously been quantified. Some of the outcomes from Table 1 for
which global WASH-attributable disease burden estimates are available
(right column) are not included in this analysis as high quality evidence
on the exposure-response relationship is lacking.
This disease burden assessment for the year 2016 preferably in-
cludes adverse health outcomes for which the WASH-attributable
fraction of disease burden can be estimated using comparative risk
assessment (CRA, respective diseases are diarrhoea, ARI and schisto-
somiasis). CRAs are based on detailed, i.e., by level of exposure, age
group and sex, exposure and exposure-response information (Ezzati
et al., 2002;WHO, 2004). In addition, we present WASH-attributable
disease burden estimates from other health outcomes for which suffi-
cient exposure and exposure-response data was available but which are
based on weaker evidence, more assumptions and different counter-
factual exposure distributions (malnutrition, malaria, soil-transmitted
helminth infections and trachoma). WASH-attributable burden of dis-
ease estimates were calculated for 132 low- and middle-income coun-
tries as the available epidemiological evidence originates mainly from
these settings. For diarrhoea (only for hygiene as risk factor) and acute
respiratory infections, estimates were calculated for 183 low-, middle-
and high-income countries. Countries are WHO Member States with
income levels defined by the World Bank for 2016 (World Bank, 2016)
which were grouped into the six WHO Regions (Sub-Saharan Africa,
America, Eastern Mediterranean, Europe, South-East Asia, and Western
Pacific (WHO, 2017a)). Data on total deaths and disability-adjusted life
years (DALYs) by disease or condition were taken from the WHO Global
Health Observatory for the year 2016 (WHO, 2018a).These data are
Table 1
Adverse health outcomes that are at least partly attributable to inadequate water, sanitation and hygiene behaviours.
Global WASH-attributable disease burden not quantified Global WASH-attributable disease burden estimates available
Health outcomes Health outcomes Main WASH exposure
Arsenicosis
Cyanobacterial toxins
Fluorosis
Hepatitis A, E
Lead poisonings
Legionellosis
Leptospirosis
Methaemoglobinaemia
Neonatal conditions and maternal outcomes
Poliomyelitis
Scabies
Spinal injury
Ascariasis sanitation
Cancer (bladder) drinking water
Dengue water resource management/water bodies
Diarrhoeal diseases drinking water, sanitation, hygiene behaviours*
Drowning
d
recreational water/water bodies
Hookworm disease
a
Sanitation
Japanese Encephalitis water resource management/agricultural practices
Lymphatic filariasis water resource management/water bodies
Malaria
d
water resource management/water bodies
Musculoskeletal diseases drinking water
Onchocerciasis water resource management
Protein-energy malnutrition
a,b,c
drinking water, sanitation, hygiene behaviours*
Respiratory infections
c
hygiene behaviours*
Schistosomiasis
a,b,c,d
drinking water, sanitation, hygiene behaviours*, water resource management/
agricultural practices/recreational water
Trachoma
a,c
sanitation, hygiene behaviours*
Trichuriasis
a
Sanitation
The listed diseases are based on prior work (Prüss-Ustün et al., 2016,2008). Health outcomes quantified in this article are written in bold. *hygiene behaviours
include hand hygiene(diarrhoeal diseases, protein-energy malnutrition, trachoma), face hygiene (trachoma), food hygiene (hookworm) and bathing (schistoso-
miasis).
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
2
publicly available and can be assessed from the following website
(WHO, 2018b).
2.2. Population attributable fractions of disease for individual risk factors
and for the cluster of risks
Disease burden attributable to a risk factor is estimated using the
population attributable fraction (PAF) which is the proportion of dis-
ease or death that could be prevented if exposures were reduced to an
alternative or counterfactual scenario, while other conditions remain
unchanged (Ezzati et al., 2002;WHO, 2004). The calculation of the PAF
requires the proportion of the population exposed to the different levels
of the risk factor and the corresponding exposure-response relationship
(Vander Hoorn et al., 2004):
=
+
=
=
PAF
p RR
p RR
( 1)
( 1) 1
j
n
jj
j
n
jj
1
1
(1)
where
pj
is the proportion of the population exposed at exposure level
j
,
RRj
is the relative risk at exposure level
j
and nis the total number of
exposure levels.
Exposure levels of drinking water, sanitation and hygiene are re-
lated by similar mechanisms and policy interventions. The following
formula has been proposed for the estimation of burden attributable to
a interlinked cluster of risk factors (Lim et al., 2012) (relevant for the
diarrhoea and schistosomiasis burden):
=
=
PAF PAF1 (1 )
r
R
r
1
(2)
where ris the individual risk factor, and Rthe total number of risk
factors accounted for in the cluster.
2.3. Choice of counterfactual exposure levels for WASH-attributable disease
burden estimation
The counterfactual exposure distribution can be defined in various
ways including the theoretical, the plausible, the feasible and the cost-
effective minimum risk exposure distributions (Murray et al., 2003).
The theoretical minimum risk exposure distribution refers to the ex-
posure level with the lowest population health risk, irrespective of
whether this level is currently attainable in practice. The plausible
minimum risk exposure distribution refers to a level which is imagin-
able without necessarily being likely or feasible in the near future. The
feasible minimum risk exposure distribution is a level that has been
observed in some population and the cost-effective minimum risk ex-
posure distribution considers the costs of exposure reduction for
choosing the alternative exposure scenario (Murray et al., 2003).
Depending on the type and quality of the available evidence, we
chose different definitions of the counterfactual exposure distribution
for the various adverse health outcomes included in this analysis
(Table 2). For WASH-attributable diarrhoeal disease burden estimation,
we applied the plausible minimum risk exposure distribution which
includes that all the population boils and filters their drinking water
and prevents recontamination, lives in a community in which coverage
with basic sanitation services exceeds 75% and practices handwashing
with soap after potential faecal contact. The WASH-attributable burden
of malnutrition estimates are based on the diarrhoea estimates using a
pooled analysis of the fraction of stunting attributable to repeated
diarrhoea episodes (Checkley et al., 2008). We also used the plausible
minimum risk exposure distribution for the hygiene-attributable disease
burden of acute respiratory infections. For trachoma and soil-trans-
mitted helminth infections, we used the theoretical minimum risk ex-
posure distribution and assume that the burden of these diseases could
be completely prevented through adequate WASH, based on current
knowledge on disease transmission which basically occurs through in-
adequate sanitation and hygiene. The theoretical minimum risk
Table 2
Information on counterfactual, outcome association and potential for bias by health outcome.
health outcome WASH counterfactual exposure definition prevalence of WASH
counterfactual exposure in 2016
RR for/association between WASH counterfactual exposure
and outcome# (against lowest level of exposure, e.g.,
unimproved WASH)
counterfactual definition
used
potential for bias
diarrhoea water: household water treatment using
filtering or boiling
33.1% (WHO and UNICEF,
undated)
RR 0.52 (0.35, 0.77)* (Wolf et al., 2018a) plausible minimum risk predominately non-blinded intervention
studies but bias-adjustment performed
sanitation: basic sanitation in a community
> 75% sanitation coverage
45.3% (Wolf et al., 2018c) RR 0.55 (0.34, 0.91) (Wolf et al., 2018a)
hygiene: handwashing with soap after
potential faecal contact
26.2% (Wolf et al., 2018b) RR 0.86 (0.35, 2.07)* (Wolf et al., 2018a)
acute respiratory
infections
hygiene: handwashing with soap after
potential faecal contact
26.2% (Wolf et al., 2018b) RR 0.84 (0.79, 0.89) (Rabie and Curtis, 2006) plausible minimum risk predominantly non-blinded intervention
studies
protein-energy
malnutrition
same as for diarrhoea same as for diarrhoea combining the PAF for stunting attributable to diarrhoea
(25% (8%, 38%)) (Checkley et al., 2008) with the PAF of
WASH-attributable diarrhoeal disease (60% (54%, 65%))
same as for diarrhoea includes only WASH-attributable burden
via diarrhoea, only stunting is considered as
indicator for malnutrition
schistosomiasis basic drinking water and basic sanitation
services
basic drinking water: 87.2%; basic
sanitation: 62.0% (WHO and
UNICEF, undated)
basic drinking water: RR 0.53 (0.47, 0.61) (Grimes et al.,
2014); basic sanitation: RR 0.65 (0.54, 0.78) (Freeman et al.,
2017)
feasible minimum risk RR estimates from observational studies
only
malaria safe water resource management 0% (Keiser et al., 2005) RR 0.21 (0.13–0.33) (Keiser et al., 2005) theoretical minimum risk disease burden estimates based on stronger
assumptions
soil-transmitted
helminth
infections
safely managed water and safely managed
sanitation services, essential hygiene
conditions and essential hygiene practices
NA RR 0 theoretical minimum risk disease burden estimates based on stronger
assumptions
trachoma safely managed water and safely managed
sanitation services, essential hygiene
conditions and essential hygiene practices
NA RR 0 theoretical minimum risk disease burden estimates based on stronger
assumptions
RR: relative risk, NA: not applicable, # separate RR for water, sanitation and hygiene are combined using equation (2), * adjusted for potential non-blinding bias.
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
3
exposure distribution is approximated here as all the population using
safely managed drinking water, i.e., a basic drinking water service ac-
cessible on premises, available when needed and free from con-
tamination, safely managed sanitation, i.e., a basic sanitation service
that safely disposes excreta in-situ or that ensures that excreta are safely
treated off-site, and all the population having access to essential hy-
giene conditions and performing essential hygiene practices that help
maintain health and prevent the spread of disease, including hand- and
facewashing, menstrual hygiene management and food hygiene (WHO
and UNICEF, undated). Also for the WASH-attributable malaria burden
estimates, we used the theoretical minimum risk exposure distribution
of all the population being exposed to safe water resource management
for which a corresponding exposure-response relationship from meta-
analysis is available (Keiser et al., 2005). For the WASH-attributable
schistosomiasis disease burden estimation, the applied counterfactual is
equivalent to a feasible minimum risk exposure distribution which is
access to basic drinking water and sanitation services. This is again due
to the available matching exposure-response relationships for these
exposures (Freeman et al., 2017;Grimes et al., 2014).
2.4. Estimation of burden of disease attributable to inadequate WASH
The burden of disease attributable to each risk factor (AB), or to the
cluster of risk factors, in deaths or DALYs, was obtained by multiplying
the PAF by the total burden of each respective disease (B):
AB = PAF x B (3)
The PAFs were applied equally to burden of disease in deaths and
DALYs and we assumed that the WASH-attributable case fatality was
the same as the mean case fatality of the respective diseases.
2.5. Uncertainty estimates
To estimate uncertainty intervals, we developed a Monte Carlo si-
mulation of the results with 5000 draws of the exposure distribution,
and of the relative risks. As lower and upper uncertainty estimates we
used the 2.5 and 97.5 percentiles of the PAFs, attributable deaths and
DALYs resulting from the Monte Carlo analysis. Uncertainty estimates
were calculated using @RISK-software, version 6 (@RISK, n.d.).
We are following guidelines for accurate and transparent health
estimates reporting (GATHER)(“GATHER: Guidelines for Accurate and
Transparent Health Estimates Reporting,” n.d.; Stevens et al., 2016) and
have included a GATHER-checklist as a Supplementary File (S3).
2.6. The WASH-attributable burden of diarrhoeal disease
2.6.1. Adjustment for non-blinding bias of interventions for exposure-
response estimation
Open trials – that is where participants are not blinded to their al-
location – which use subjective outcome measures, such as self-reported
diarrhoea, are at high risk of bias (Savović et al., 2012;Wood et al.,
2008). Exposure-response relationships linking point-of-use drinking
water or hygiene interventions and diarrhoea were therefore bias-ad-
justed based on empirical evidence (Savović et al., 2012)(Tables S1 and
S2 in the Supplementary File 1) using a previously published method
(Wolf et al., 2018a,2014). These two types of WASH interventions were
chosen for bias adjustment as these interventions usually aim ex-
clusively to improve health which is apparent to the recipient. A de-
tailed description of this approach can also be found in the Supple-
mentary File S1. We present WASH-attributable diarrhoeal disease
burden as bias-adjusted estimates in the main text and additionally as
non-adjusted estimates in the Supplementary File S1, Tables S3–S5, to
show the magnitude of this adjustment and for comparability with
other burden of disease assessments.
Drinking water
Fig. 1 shows drinking water exposure levels and Tables 2 and S1
(Supplementary File 1) show matching exposure-response relationships
used for WASH-attributable burden of diarrhoeal disease estimation.
Exposure estimates: Data on the relevant exposure levels was avail-
able through country-representative household surveys and censuses
reported by the JMP (WHO and UNICEF, undated). Estimates for the
year 2016 were derived using multilevel modeling (Wolf et al., 2013) of
about 1400 data points for each of the different categories of drinking
water supply and about 130 data points for each of the different cate-
gories of household water treatment. Exposure estimates for the dif-
ferent levels of drinking water relevant for burden of disease calculation
are available by country as a Supplementary File (S2).
Exposure-response relationship: As the evidence on additional im-
provements – such as improvements in water quality and availability -
on piped water to premises remains limited, we chose household water
filtering or boiling with prevention of recontamination as the coun-
terfactual exposure level. Corresponding exposure-response relation-
ships were taken from the most recent meta-analysis (Wolf et al.,
2018a). (Tables 2 and S1 in the Supplementary File 1)
Sanitation
Fig. 2 shows sanitation exposure levels. Tables 2 and S2 (Supple-
mentary File 1) shows the matching exposure-response relationship
used for WASH-attributable burden of diarrhoeal disease estimation.
Exposure estimates: Sanitation exposure data was available from the
JMP (WHO and UNICEF, undated). Exposure estimates of access to
basic sanitation services in a community with greater than 75% cov-
erage with basic sanitation services is based on an analysis of survey
data at cluster-level (Wolf et al., 2018c). Exposure estimates for the
different levels of sanitation relevant for burden of disease calculation
are available by country as a Supplementary File (S2).
Exposure-response relationship: New evidence has recently emerged
on additional benefits on diarrhoeal disease from safe sanitation when
people live in communities with high sanitation coverage (e.g., (Fuller
and Eisenberg, 2016;Jung et al., 2017b,2017a)). This has led to using
basic sanitation services in a community in which more than 75% of
people are covered with basic sanitation services as the counterfactual
exposure scenario. The choice of the cut-off at 75% sanitation coverage
is based on prior sanitation intervention studies which found increased
diarrhoea reductions after that point (Wolf et al., 2018c,2018a).
As a sensitivity analysis, we included the recently published results of
four WASH intervention studies (Humphrey et al., 2019;Luby et al., 2018;
Null et al., 2018;Reese et al., 2018) in the calculation of the exposure-
response relationship between inadequate sanitation and diarrhoeal dis-
ease. Results of these studies had not been published at the time of the
systematic review and meta-analysis that provided the exposure-response
relationships for this burden of disease assessment (Wolf et al., 2018a).
Hygiene
Fig. 2 shows hygiene exposure levels and Tables 2 and S2 (Sup-
plementary File 1) show matching exposure-response relationships used
for burden of disease estimation.
Exposure estimates: Exposure estimates are based on “having a
handwashing facility with soap and water on premises”, i.e., a basic
handwashing facility (WHO and UNICEF, 2018a), and are available
through country-representative household surveys such as Demo-
graphic Health Surveys and Multiple Indicator Cluster Surveys through
the JMP (WHO and UNICEF, undated). Because access to a basic
handwashing facility would overestimate actual handwashing prac-
tices, this proxy indicator has been converted to actual handwashing
with soap prevalence based on an analysis of the association between
presence of a basic handwashing facility and observed handwashing
with soap (Wolf et al., 2018b). Exposure estimates for handwashing
with soap after potential faecal contact are available by country as a
Supplementary File (S2).
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
4
Exposure-response relationship: The relative risk from a recent sys-
tematic review and meta-analysis of WASH intervention studies and
diarrhoeal disease (Wolf et al., 2018a) associated with the sub-group of
studies focusing on “handwashing promotion” matched best the ex-
posure and was therefore taken for burden of disease calculation.
2.7. The WASH-attributable burden of further selected health outcomes
2.7.1. Acute respiratory infections
Hands act frequently as carriers for respiratory pathogens which can
enter the body via hand-to-face contact (Warren-Gash et al., 2013). In
addition, some forms of respiratory viral disease are transmitted via the
faecal-oral route (Rabie and Curtis, 2006).
Exposure estimates: Only inappropriate hygiene is considered as risk
factor for acute respiratory infections. The same hygiene exposure data
as for the analysis of the WASH-attributable diarrhoeal disease burden
were taken (handwashing with soap after potential faecal contact de-
rived from access to a handwashing facility with soap and water (Wolf
et al., 2018b)).
Exposure-response relationship: The relative risk of 0.84 for washing
hands with soap and respiratory infections is based on a meta-analysis
of seven intervention studies in high-income countries (HICs) (Rabie
and Curtis, 2006) which is similar to a more recent pooled estimate
from low- and middle-income countries (LMICs) based on only three
studies (Mbakaya et al., 2017). Only one of the seven hand-hygiene
intervention studies was blinded and used a placebo hand-sanitizer in
the control group (White et al., 2001).
2.7.2. Protein-energy malnutrition
Inadequate WASH can be linked to nutritional status via diarrhoea,
environmental enteropathy, (subclinical) enteropathogen infections
and soil-transmitted helminth infections (Dangour et al., 2013;MAL-ED
Network Investigators, 2017;Schnee et al., 2018).
Exposure estimates: As the WASH-attributable malnutrition estimates
are based on the WASH-attributable diarrhoea estimates, the same ex-
posure levels are used as for the WASH-attributable diarrhoeal disease
burden estimation.
Exposure-response relationship: A pooled analysis of nine prospective
datasets from five countries estimated that 25% of stunting could be
attributed to repeated diarrhoea episodes in children (Checkley et al.,
2008). This estimate is combined with the fraction of WASH-attribu-
table diarrhoeal disease burden in children under five to estimate the
fraction of the WASH-attributable malnutrition burden.
As a sensitivity analysis, disease burden of protein-energy mal-
nutrition was calculated using diarrhoea estimates that were not ad-
justed for non-blinding bias.
2.7.3. Schistosomiasis
Schistosomiasis can occur when people contact water containing
certain aquatic snails that have been infested with parasitic worms;
these worms have a human life cycle and are discharged through
human excreta (WHO, 2018c).
Exposure estimates: The relevant exposure levels for the analysis of
the WASH-attributable schistosomiasis burden were use of basic
drinking water and sanitation services and surface, unimproved or
limited drinking water and open defecation, unimproved or limited
sanitation. Data on these exposures were available through the JMP
Fig. 1. Exposure levels for drinking water-related burden of diarrhoeal disease
estimates.
Note: these exposure levels are used for the WASH-attributable burden of
diarrhoeal disease assessment, exposure levels used for the assessment of other
diseases vary. “limited”, “unimproved” and “basic” facilities and services follow
definitions of the WHO/UNICEF Joint Monitoring Programme for Water
Supply, Sanitation and Hygiene (JMP) (WHO and UNICEF, undated). “Coun-
terfactual” signifies the counterfactual exposure distribution used for the diar-
rhoeal disease assessment and presents the plausible minimum exposure dis-
tribution. The theoretical minimum risk exposure distribution (which is not
used for this analysis) would be “safely managed drinking water”. The length of
the different arrows in not intended to quantify differences in disease risk.
Fig. 2. Exposure levels for sanitation-related (left) and hygiene-related (right)
burden of disease estimates.
Note: these exposure levels are used for the WASH-attributable burden of
diarrhoeal disease and – for hygiene - acute respiratory infections assessment,
exposure levels used for burden of disease estimation of other diseases vary.
“limited”, “unimproved” and “basic” facilities and services follow definitions of
the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation
and Hygiene (JMP) (WHO and UNICEF, undated). “Counterfactual” signifies
the counterfactual exposure distribution used for the diarrhoeal disease and
respiratory infections assessment and presents the plausible minimum exposure
distribution. The theoretical minimum risk exposure distribution (which is not
used for the diarrhoea and respiratory infections analysis) would be “Safely
managed sanitation” and “Essential hygiene conditions and practices including
hand- and facewashing, menstrual hygiene management and food hygiene”.
The length of the different arrows in not intended to quantify differences in
disease risk.
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5
(WHO and UNICEF, undated) with estimates derived for 2016 as de-
scribed for diarrhoea (Wolf et al., 2013)(Supplementary File S2).
Exposure-response relationship: The pooled relative risk from meta-
analysis of 0.53 (0.47, 0.61) links access to basic drinking water ser-
vices versus surface, unimproved or limited drinking water (Grimes
et al., 2014). The pooled relative risk of 0.65 (0.54, 0.78) for sanitation
links basic sanitation services and open defecation, unimproved or
limited sanitation and is the mean relative risk combining the asso-
ciation between sanitation and Schistosoma mansoni and S. haematobium
weighted by the precision of the estimates (Freeman et al., 2017). These
relative risks include data from observational studies only (cross-sec-
tional and case-control design).
As a sensitivity analysis we calculated the WASH-attributable
schistosomiasis burden using a population attributable fraction (PAF) of
82% as previously estimated through an expert survey (Prüss-Ustün
et al., 2016). This 82% relates to the fraction of schistosomiasis that was
assumed to be preventable through adequate WASH while it was ac-
knowledged that probably 100% of schistosomiasis burden could be
attributed to environmental risks (Prüss-Ustün et al., 2016).
2.7.4. Malaria
Environmental management in malaria prevention often includes
water resource management - for example, the installation, cleaning
and maintenance of drains, the systematic elimination of standing
water pools, the siting of settlements away from vector breeding sites
(dry-belting) - but also measures applied to the human habitat such as
mosquito-proofing of houses (Keiser et al., 2005).
Exposure estimates: Globally, very limited water resource manage-
ment have been undertaken and environmental management inter-
ventions almost disappeared when dichlorodiethyltrichloroethane
(DDT) appeared (Keiser et al., 2005). Therefore the relevant exposure
levels are universally implemented safe water resource management as
theoretical minimum risk exposure distribution versus no safe water
resource management.
Exposure-response relationship: The exposure-response relationship
is taken from a meta-analysis of the relation between environmental
management and malaria occurrence (Keiser et al., 2005). We chose the
more conservative – in terms of the size of the relative risk estimate -
approach which was based on stronger evidence, and selected an ex-
posure-response relationship (risk ratio) of 0.21 (0.13–0.33) for mod-
ification of human habitation – as compared to 0.12 (0.08, 0.18) for
environmental modification.
As a sensitivity analysis we calculated the WASH-attributable ma-
laria burden using previously estimated regional PAFs that were based
on expert opinion (Prüss-Ustün et al., 2016).
2.7.5. Soil-transmitted helminth infections
This assessment includes the most predominant soil-transmitted
helminths – Ascaris lumbricoides,Trichuris trichiura and the hookworms.
Transmission occurs uniquely through the release of nematode eggs in
human excreta from infected individuals into the environment. After
the release from the human body, the eggs need to mature for about
three weeks to become infective. Susceptible individuals are infected
via ingestion of these eggs or penetration of their skin by, or direct
ingestion of, the larvae. Also re-infection only occurs due to contact
with infective stages in the environment (WHO, 2018d). It is therefore
assumed that infections with soil-transmitted helminths would com-
pletely cease in case the theoretical minimum exposure level – universal
use of safely managed water and safely managed sanitation services,
universal access to essential hygiene conditions and universal practice
of essential hygiene - would be achieved. The total disease burden from
infections with soil-transmitted helminths was therefore entirely at-
tributed to inadequate WASH (Prüss-Ustün et al., 2016).
2.7.6. Trachoma
Trachoma is transmitted via personal contact (e.g., via hands and
clothes) and by flies that have been in contact with the discharge of the
eyes or the nose of an infected person (WHO, 2018e). It is assumed that
through safe disposal of faeces and especially hygiene (face- and
handwashing and cleaning of clothes) transmission of trachoma would
cease which is also supported through historical evidence (Hu et al.,
2010;Mohammadpour et al., 2016). The overall disease burden from
trachoma was therefore assumed to be fully attributable to inadequate
WASH (Prüss-Ustün et al., 2016). For trachoma, we used the same
theoretical minimum exposure level as for soil-transmitted helminths of
universal safely managed drinking water, safely managed sanitation,
essential hygiene conditions and hygiene practices.
3. Results
3.1. Exposure estimates
The relevant exposures for WASH-attributable disease burden esti-
mation include access to services and WASH-related behaviours. Water
resource management is the relevant exposure for WASH-attributable
burden of malaria estimation. In LMICs, 58% of the population used
piped water on premises; 30% used a non-piped basic water service;
and 13% used surface, unimproved or limited drinking water in 2016
(Table 3). 33% of the population reported boiling or filtering their
water. In LMICs, 62% used basic sanitation services and 45% of the
population lived in communities with basic sanitation coverage above
75% (Table 4). Worldwide, 74% of the population had access to a basic
handwashing facility, 70% in LMICs and 95% in HICs. This resulted in
26% of the global population, 22% in LMICs and 51% in HICs, washing
hands with soap after potential faecal contact (Table 5).
3.2. Estimates of the WASH-attributable burden of diarrhoeal disease
The total number of diarrhoeal deaths in 2016 was 1.4 million
(WHO, 2018f). Of those, 485,000 deaths were attributable to in-
adequate water, 432,000 to inadequate sanitation and 165,000 to in-
adequate hygiene behaviours after adjusting for the likely effect of non-
blinding bias (Tables 6–9). Inadequate WASH together caused 829,000
diarrhoeal deaths which correspond to about 60% of total diarrhoeal
deaths in 2016 that would have been preventable through improving
drinking water and sanitation services and handwashing with soap.
In children under five years of age, 477,000 diarrhoeal deaths oc-
curred in 2016. Of those 297,000 or 62.2% (adjusted for non-blinding
bias) were attributable to inadequate WASH.
Not adjusting the disease burden estimates for non-blinding bias
resulted in a total of 1,025,000 deaths which correspond to 74% of total
diarrhoeal deaths and 1.8% of all deaths being attributable to in-
adequate WASH in 2016 (Supplementary File S1, Tables S3–S5).
Inclusion of the results of four additional WASH interventions
(Humphrey et al., 2019;Luby et al., 2018;Null et al., 2018;Reese et al.,
2018) published after we conducted the systematic review and meta-
analysis on WASH interventions and diarrhoeal disease (Wolf et al.,
2018a), changed the exposure-response relationship for basic sanitation
in low-coverage communities to 0.82 (0.63, 1.06) and in high coverage
communities to 0.58 (0.40, 0.84) as compared to 0.76 (0.51, 1.13) and
0.55 (0.34, 0.91) for low- and high-coverage communities respectively
without these four studies (Tables 2 and S2 in the Supplementary File
1). This resulted in a reduction of diarrhoeal deaths attributable to
inadequate sanitation from 432,000 to 396,000.
3.3. Estimates of the WASH-attributable burden of other adverse health
outcomes
3.3.1. Acute respiratory infections
Thirteen percent of the overall disease burden of acute respiratory
infections was attributable to inadequate handwashing with soap which
amounted to 370,000 deaths in 2016 (Table 10). WASH-attributable
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6
disease burden from acute respiratory infections by region is given in
Table S6 in the Supplementary File 1.
3.3.2. Protein-energy malnutrition
Combining the fraction of diarrhoeal disease burden attributed to
inadequate WASH in children below five years of age (adjusted esti-
mate) with the estimate of 25% of stunting attributable to repeated
diarrhoea episodes by country (Checkley et al., 2008) resulted in the
attribution of 16% of malnutrition to inadequate water, sanitation and
hygiene for 2016 (Table 10). These estimates do not include the con-
sequences of protein-energy malnutrition on other diseases and asso-
ciated mortality. WASH-attributable disease burden from protein-en-
ergy malnutrition by region is given in Table S7 in the Supplementary
File 1.
Using non-adjusted diarrhoea estimates to calculate the WASH-at-
tributable protein-energy malnutrition burden resulted in the attribu-
tion of 20% of malnutrition to inadequate WASH and in 34,000 WASH-
attributable deaths in children below five years of age (Supplementary
File S1, Table S8).
3.3.3. Schistosomiasis
Using the available exposure and exposure-response information, it is
estimated that 43% or 10,400 deaths could have been prevented by im-
proving drinking water and sanitation services in 2016 (Table 10). In-
adequate drinking water is responsible for 5700 deaths and inadequate
sanitation for 6300 deaths. WASH-attributable disease burden from
schistosomiasis by region is given in Table S9 in the Supplementary File 1.
The sensitivity analysis using the previously estimated PAF of 82%
based on expert survey (Prüss-Ustün et al., 2016) would result in about
20,000 WASH-attributable Schistosomiasis deaths.
3.3.4. Malaria
It is estimated that 80% of malaria was attributable to non-existent
water resource management which resulted in 355,000 WASH-attri-
butable malaria deaths in 2016 (Table 10).
A sensitivity analysis using previously estimated regional PAFs for
malaria that were based on expert survey (Prüss-Ustün et al., 2016)
resulted in 187,000 WASH-attributable malaria deaths in 2016.
3.3.5. Soil-transmitted helminth infections and trachoma
Assuming 100% of soil-transmitted helminth infections and tra-
choma cases are attributable to inadequate WASH, over 6000 deaths
could have been prevented in 2016 through safely managed water and
sanitation, access to essential hygiene conditions and practice of es-
sential hygiene behaviours (Table 10).
WASH-attributable disease burden estimates (in deaths and DALYs)
by country and health outcome is detailed in Supplementary Files S4
(deaths) and S5 (DALYs).
4. Discussion
It is estimated that 1.6 million deaths and 105 million DALYs are
attributable to inadequate WASH, including only diseases which could
be quantified, representing 2.8% of total deaths and 3.9% of total
DALYs in 2016. Of those, 829,000 deaths are due to diarrhoeal disease.
Sixty per cent of the overall diarrhoea burden, 13% of the burden from
acute respiratory infections, 16% of the burden of protein-energy
malnutrition, 43% of the schistosomiasis burden, 80% of the malaria
burden and 100% of both the burden from soil-transmitted helminth
infections and trachoma burden are attributed to inadequate WASH.
4.1. Discussion of results
Compared to our previous burden of diarrhoeal disease assessment
Table 3
Distribution of the population to exposure levels of drinking water, by region, for 2016.
Region Percentage of population using Total
piped water on premises basic drinking water, not piped on premises surface, unimproved or limited water
not filtered
or boiled
a
filtered or boiled not filtered
or boiled
filtered or boiled not filtered
or boiled
filtered or boiled
Sub-Saharan Africa, LMICs 25.5 3.1 29.6 2.0 35.8 4.0 100
America, LMICs 58.3 32.3 4.6 1.1 2.9 0.8 100
Eastern Mediterranean, LMICs 53.8 4.8 26.0 0.7 13.7 0.9 100
Europe, LMICs 55.6 29.3 6.9 4.1 2.5 1.7 100
South-East Asia, LMICs 24.9 12.7 38.6 13.0 7.2 3.5 100
Western Pacific, LMICs 28.5 50.7 8.8 8.3 1.6 2.1 100
Total LMICs 34.1 23.5 22.6 7.0 10.2 2.6 100
a
Filtering or boiling means point-of-use water treatment at household-level. The total may not equal the sum of numbers displayed in the rows due to rounding.
LMICs: low- and middle-income countries.
Table 4
Distribution of the population to exposure levels of sanitation, by region, for
2016.
Region Percentage of population
using basic
sanitation
services
living in communities with
> 75% basic sanitation
coverage
Sub-Saharan Africa, LMICs 30.8 13.3
America, LMICs 85.1 75.8
Eastern Mediterranean,
LMICs
69.1 54.8
Europe, LMICs 92.5 93.3
South-East Asia, LMICs 50.9 31.9
Western Pacific, LMICs 75.1 63.2
Total LMICs 62.0 45.3
LMICs: low and middle income countries.
Table 5
Distribution of the population to exposure levels of hygiene, by region, for
2016.
Region Percentage of population washing hands with
soap after potential faecal contact
Sub-Saharan Africa, all 8.4
America, LMICs 36.2
Eastern Mediterranean, LMIC 21.6
Europe, LMICs 24.9
South-East Asia, all 27.8
Western Pacific, LMICs 17.1
Total 26.3
Total HICs 50.6
Total LMICs 21.8
LMICs: low and middle income countries, HICs: high income countries.
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
7
for the year 2012 (Prüss-Ustün et al., 2014), we now attribute about
17,000 less deaths to inadequate water (2012: 502,000 deaths, 2016:
485,000 deaths), 152,000 additional deaths to inadequate sanitation
(2012: 280,000 deaths, 2016: 432,000 deaths) and 132,000 less deaths
to inadequate hygiene behaviours (2012: 297,000 deaths, 2016:
165,000 deaths). Especially the methods for exposure assessment of
both inadequate sanitation and inadequate hygiene behaviours have
been revised using updated evidence. The consideration of health im-
pacts from poor sanitation coverage in the community led to a sig-
nificant increase of disease burden from inadequate sanitation. Fur-
thermore, we are no longer relying on observations of handwashing
frequency which are usually not nationally representative. Diarrhoea
deaths attributable to inadequate WASH also changed due to reductions
in overall diarrhoeal mortality (WHO, 2018a) and updated exposure-
response relationships (Wolf et al., 2018a).
For comparison with similar estimates, the comparative risk as-
sessment for the year 2016 for the Global Burden of Disease Study
conducted by the Institute for Health Metrics and Evaluation attributed
89% of diarrhoea deaths and 8% of deaths from acute respiratory in-
fections to inadequate WASH (Gakidou et al., 2017) – compared to 60%
and 13% in this assessment. Differences compared to our estimates are
mainly due to our approach of adjusting some WASH interventions for
non-blinding bias (only diarrhoeal disease burden estimates, see dis-
cussion below), different approaches of exposure assessment and dif-
ferent minimum risk exposure (counterfactual) levels. The Institute for
Health Metrics and Evaluation considers sewered sanitation as the sa-
nitation counterfactual, which is however not necessarily supported by
recent evidence nor for rural areas (Baum et al., 2013;WHO and
UNICEF, 2017). Community sanitation coverage is not taken into ac-
count and availability of basic handwashing facilities is used as ex-
posure parameter which does not match the parameter of the exposure-
response relationship which is handwashing with soap at times of po-
tential pathogen exposure.
Recent WASH disease burden estimates have varied considerably: in
2010 the Global Burden of Disease Study estimated 337,000 deaths
from inadequate WASH (Lim et al., 2012) while subsequently reporting
1,399,000 deaths in 2013 (Forouzanfar et al., 2015), 1,766,000 deaths
in 2015 (Forouzanfar et al., 2016), 1,661,000 deaths in 2016 (Gakidou
et al., 2017) and 1,610,000 in 2017 (Stanaway et al., 2018). The initial
increase was mainly due to the fact that the first counterfactuals for
estimating WASH-attributable burden of disease were improved
drinking water sources and improved sanitation facilities as defined by
the JMP (WHO and UNICEF, undated). Improved drinking water
sources are often unreliable and of poor water quality while improved
sanitation is often not safely managed and does not protect the com-
munity (Bain et al., 2014;Clasen et al., 2014;WHO and UNICEF, 2017).
More recent WASH-attributable global burden of disease assessments
recognize health impacts from improvements in drinking water and
sanitation beyond improved water sources and sanitation facilities, i.e.,
piped water sources, household water treatment and sewered sanita-
tion, and from considering personal hygiene as separate risk factor.
Since the 2015 assessment, more diseases have been added in the
Global Burden of Disease assessments such as typhoid and paratyphoid
fever in 2015 (Forouzanfar et al., 2016) and acute respiratory infections
in 2016 and 2017 (Gakidou et al., 2017;Stanaway et al., 2018).
The positive side of a high WASH-attributable disease burden is the
great potential for disease burden reduction. In theory, the entire esti-
mated disease burden could have been prevented through interven-
tions. These interventions vary depending on the health outcome and
the chosen counterfactual exposure distribution. Diarrhoea, acute re-
spiratory infections, malnutrition and schistosomiasis will require im-
provements of drinking water and sanitation services and increased
handwashing with soap. The same is true for soil-transmitted helminth
infections and trachoma, however to completely prevent these infec-
tions more radical and comprehensive WASH interventions are required
(safely managed drinking water and sanitation services, access to es-
sential hygiene conditions and practice of essential hygiene beha-
viours). Additionally, the prevention of soil-transmitted helminth in-
fections might require the proper treatment of human waste and
adequate food hygiene to prevent infections that occur through the use
of human faeces as fertilizer (Anuar et al., 2014;Strunz et al., 2014).
Trachoma prevention might include the need for a stronger emphasis
on comprehensive hygiene practices including facewashing (Stocks
et al., 2014). Finally to reduce the WASH-attributable malaria disease
Table 6
Diarrhoea burden attributable to inadequate water by region, 2016
Region PAF (95% CI) Deaths (95% CI) DALYs (in 1 000s) (95% CI)
Sub-Saharan Africa, LMICs 0.40 (0.22–0.51) 259,073 (140,144–330,643) 16,837 (9120–21,472)
America, LMICs 0.27 (0.02–0.42) 6246 (480–9469) 506 (22–776)
Eastern Mediterranean, LMICs 0.39 (0.19–0.50) 48,947 (24,067–63,413) 3675 (1778–4764)
Europe, LMICs 0.20 (0.02–0.31) 959 (86–1500) 137 (2–215)
South-East Asia, LMICs 0.31 (0.12–0.43) 163,760 (64,307–225,941) 7798 (3067–10,750)
Western Pacific, LMICs 0.21 (0.08–0.30) 5756 (2069–8320) 493 (160–725)
Total LMICs 0.36 (0.19–0.47) 484,741 (231,153–639,285) 29,446 (14,149–38,702)
DALYs: disability-adjusted life years, PAF: population-attributable fraction; LMICs: low- and middle-income countries; for the analysis of burden of diarrhoeal disease
attributed to inadequate water the counterfactual exposure distribution (plausible minimum risk) of filtering/boiling of water from any water source with subsequent
safe storage was compared to the actual exposure distribution for 2016.
Table 7
Diarrhoea burden attributable to inadequate sanitation by region, 2016
Region PAF (95% CI) Deaths (95% CI) DALYs (in 1 000s) (95% CI)
Sub-Saharan Africa, LMICs 0.37 (0.36–0.38) 236,134 (229,625–241,875) 15,303 (14,866–15,684)
America, LMICs 0.14 (0.13–0.16) 3261 (2949–3529) 257 (229–280)
Eastern Mediterranean, LMICs 0.27 (0.24–0.30) 34,425 (30,473–37,781) 2538 (2260–2775)
Europe, LMICs 0.03 (0.02–0.03) 134 (91–161) 20 (14–24)
South-East Asia, LMICs 0.29 (0.25–0.33) 152,986 (129,778–173,011) 7245 (6131–8208)
Western Pacific, LMICs 0.17 (0.15–0.20) 4780 (4041–5413) 403 (332–464)
Total LMICs 0.32 (0.30–0.34) 431,720 (407,090–452,623) 25,765 (24,519–26,825)
DALYs: disability-adjusted life years, PAF: population-attributable fraction; LMICs: low- and middle-income countries; for the analysis of burden of diarrhoeal disease
attributed to inadequate sanitation the counterfactual exposure distribution (plausible minimum risk) of having access to basic sanitation in a community with >
75% coverage with basic sanitation facilities was compared to the actual exposure distribution for 2016.
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
8
burden, interventions will be required that lead to environmental
modification and manipulation, including water resource management
as main component, and changes of the human habitat, including siting
of settlements away from breeding sites (Keiser et al., 2005).
4.2. Limitations
This WASH-attributable burden of disease assessment is limited to
some selected diseases and adverse health outcomes and does not take
into account a large amount of other adverse health outcomes (ex-
amples are given in Table 1) that are at least partly WASH-attributable
and that could be prevented through improved WASH management.
Additionally, the here presented estimates do not capture disease
burden from, for example, water-borne disease outbreaks, flooding and
droughts or disease burden in certain populations such as refugees,
internally displaced persons, and the homeless or certain exposure
settings such as healthcare facilities, schools, workplaces and other
public places. Additionally, adequate WASH and treatment of waste-
water (from households, intensive livestock raising and industry) can
reduce environmental drivers of antimicrobial resistance (Bürgmann
et al., 2018;O'Neill, 2016;WHO, 2014), an increasingly serious threat
to global public health (WHO, 2018g). WASH-attributable disease
burden estimates refer predominantly to LMICs as most of the epide-
miological evidence originates from these countries.
This analysis considers WASH-attributable deaths and DALYs from a
range of diseases and conditions including diarrhoea, acute respiratory
infections, protein-energy malnutrition, schistosomiasis, malaria, soil-
transmitted helminth infections and trachoma. Some WASH-attribu-
table disease burden estimates, i.e., for diarrhoea and respiratory in-
fections, are based on CRA and the exposure-response relationship on
meta-analysis of intervention studies. The remaining diseases have been
estimated using more limited exposure or exposure-response
information which required more assumptions. WASH-attributable
disease burden estimates for the latter diseases include therefore
greater uncertainties. The WASH-attributable estimates of the burden of
respiratory infections are calculated using a dose-response relationship
from intervention studies not adjusted for likely bias due to non-
blinding. The malnutrition estimates are based on the diarrhoea esti-
mates and therefore omit other pathways through which WASH can
have an impact on malnutrition such as subclinical enteric infections
and environmental enteropathy (Rogawski and Guerrant, 2017). In
addition, these estimates include only stunting and omit other forms of
malnutrition such as underweight and wasting. Stunting, compared to
wasting and underweight, is the more severe form of malnutrition, is
associated with chronic and recurrent undernutrition, e.g., from fre-
quent infectious disease, and prevents children from reaching their
physical and cognitive potential (WHO, 2018h). There is usually con-
siderable overlap between stunting, wasting and underweight (Myatt
et al., 2018). The estimate of the fraction of WASH-attributable stunting
is based on the fraction of stunting attributable to repeated diarrhoea
episodes (Checkley et al., 2008) which is combined with the fraction of
WASH-attributable diarrhoea. In young children from low-income
countries (where the bulk of the global burden of diarrhoea occurs)
repeated diarrhoea episodes are the norm: e.g., children under three
years old experience on average three episodes of diarrhoea every year
(WHO, 2017b). Recent findings from the GEMS study suggested that
children with both moderate/severe and less-severe diarrhoea had a
significantly increased risk for stunting (Kotloff et al., 2019). Global
health estimates for diarrhoeal disease burden which are used for
WASH-attributable disease burden estimation can be subject to con-
siderable under-reporting, especially for countries without well-func-
tioning death registration systems for which estimates rely heavily on
surveys and censuses (WHO, 2018i).Our estimate of 16% of malnutri-
tion is broadly consistent with a Cochrane review that concluded that
Table 8
Diarrhoea burden attributable to inadequate hygiene behaviours by region, 2016
Region PAF (95% CI) Deaths (95% CI) DALYs (in 1 000s) (95% CI)
Sub-Saharan Africa, all 0.13 (0–0.61) 85,166 (0–394,782) 5516 (0–25,622)
America, LMICs 0.10 (0–0.47) 2227 (0–10,741) 183 (0–886)
America, HICs 0.08 (0–0.41) 930 (0–4967) 25 (0–131)
Eastern Mediterranean, LMICs 0.12 (0–0.57) 15,013 (0–72,270) 1130 (0–5440)
Eastern Mediterranean, HICs 0.08 (0–0.41) 34 (0–186) 5 (0–27)
Europe, LMICs 0.11 (0–0.54) 537 (0–2605) 72 (0–352)
Europe, HICs 0.08 (0–0.40) 1216 (0–6371) 29 (0–151)
South-East Asia, all 0.11 (0–0.50) 56,419 (0–264,975) 2656 (0–12,477)
Western Pacific, LMICs 0.12 (0–0.55) 3347 (0–15,182) 298 (0–1350)
Western Pacific, HICs 0.08 (0–0.40) 310 (0–1645) 6 (0–31)
Total 0.12 (0–0.56) 165,200 (0–780,443) 9919 (0–46,598)
DALYs: disability-adjusted life years, PAF: population-attributable fraction; LMICs: low- and middle-income countries, HICs: high-income countries; for the analysis
of burden of diarrhoeal disease attributed to inadequate hygiene behaviours the counterfactual exposure distribution (plausible minimum risk) of handwashing with
soap after potential faecal contact was compared to the actual exposure distribution for 2016.
Table 9
Diarrhoea burden attributable to the cluster of inadequate water, sanitation and hygiene behaviours by region, 2016
Region PAF (95% CI) Deaths (95% CI) DALYs (in 1 000s) (95% CI)
Sub-Saharan Africa, all 0.67 (0.62–0.72) 431,700 (398,398–462,156) 27,997 (25,822–29,968)
America, LMICs 0.43 (0.35–0.51) 9861 (8050–11,623) 799 (639–952)
America, HICs 0.08 (0.00–0.25) 930 (0–4967) 25 (0–131)
Eastern Mediterranean, LMICs 0.60 (0.50–0.70) 76,387 (62,928–87,982) 5718 (4787–6531)
Eastern Mediterranean, HICs 0.08 (0.00–0.25) 34 (0–186) 5 (0–27)
Europe, LMICs 0.31 (0.22–0.39) 1481 (1053–1899) 207 (148–265)
Europe, HICs 0.08 (0.00–0.17) 1216 (0–6371) 29 (0–151)
South-East Asia, all 0.56 (0.43–0.68) 295,070 (225,467–356,569) 13,981 (10,634–16,948)
Western Pacific, LMICs 0.43 (0.32–0.53) 11,661 (8651–14,501) 1008 (715–1282)
Western Pacific, HICs 0.08 (0.00–0.23) 310 (0–1645) 6 (0–31)
Total 0.60 (0.54–0.65) 828,651 (753,021–901,072) 49,774 (45,835–53,596)
DALYs: disability-adjusted life years, PAF: population-attributable fraction; LMICs: low- and middle-income countries, HICs: high-income countries.
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
9
WASH interventions might have a small benefit on length growth
(Dangour et al., 2013). The schistosomiasis exposure-response function
is based on observational studies only (Freeman et al., 2017;Grimes
et al., 2014) and the counterfactual exposure distribution is use of basic
water and sanitation services which represents a feasible minimum risk
exposure distribution only. The counterfactual exposure distribution for
malaria – universal exposure to safe water resource management
(Keiser et al., 2005) – differs from the exposure distributions of the
other diseases which are related to the use of certain WASH services.
From the above it can be concluded that our disease burden estimates
are likely underestimating the true disease burden of inadequate
WASH.
While some have argued that the counterfactual exposure distribu-
tion used for risk factor-attributable disease burden estimation should
represent what can be achieved through interventions (Greenland,
2002;Steenland and Armstrong, 2006), others advocate the use of
multiple exposure distributions including those which might not be
achievable by currently available interventions to appreciate the size of
the problem (Murray et al., 2003). Based on the available evidence –
especially regarding the exposure-response relationship – our WASH-
attributable disease burden estimates are based on different – including
feasible, plausible and theoretical minimum risk – counterfactual defi-
nitions. Especially the feasible (only used for schistosomiasis) but also
the plausible minimum risk exposure levels represent interim levels on
which further improvements are possible and necessary. These interim
exposure levels should be replaced with the theoretical minimum risk
exposure distribution of safely managed water and sanitation, access to
essential hygiene conditions and practice of essential hygiene beha-
viours when the available evidence allows this. The JMP currently
provides country-level data for access to safely managed drinking water
and sanitation services only for a limited number of countries (WHO
and UNICEF, 2018b). In addition, there is to date no matching ex-
posure-response relationship from meta-analysis between safely man-
aged drinking water or sanitation and disease outcome. Even the the-
oretical minimum risk exposure distribution might underestimate the
true WASH-attributable disease burden which is supported by evidence
of residual WASH-attributable diarrhoea burden in high-income coun-
tries (Gunnarsdottir et al., 2012;Setty et al., 2017). Evidence on health
impacts of Water Safety Plans which are implemented increasingly
throughout the world (WHO and IWA, 2017) could potentially
strengthen the theoretical minimum risk exposure distribution for
burden of disease assessment and add estimates for high-income
countries in the future (Gunnarsdottir et al., 2012;Setty et al., 2017).
Exposure levels do also not include bottled or packaged water which is
used increasingly in many countries (statista, 2016). Bottled water was
frequently shown to be of high microbial quality (Bain et al., 2014;
Fisher et al., 2015;UNICEF and WHO, 2015;Williams et al., 2015;
Wright et al., 2016) and was associated with a decreased risk for
diarrhoea compared to piped water (Sima et al., 2012). Both country-
level exposure data and the matching exposure-response relationship
between bottled water use and health outcome are currently lacking.
Changing from a feasible or plausible minimum risk exposure level to a
theoretical minimum risk exposure level as the counterfactual for
WASH-attributable disease burden estimation (relevant for diarrhoea,
acute respiratory infections, malnutrition, and schistosomiasis) might
considerably increase WASH-attributable disease burden estimates.
This is supported by historical evidence of large reductions of child and
overall mortality following improvements towards safely managed
water and sanitation infrastructure in high-income countries (Alsan and
Goldin, 2018;Bell and Millward, 1998;Cutler et al., 2006).
The WASH-attributable burden of disease assessment from most
included diseases is based on WASH interventions, many of which were
poorly implemented, had low compliance and promoted or installed
technologies with disputable effectiveness. Therefore, the estimated
WASH-attributable disease fractions can be interpreted as estimates of
the fractions of disease preventable through implementing these
Table 10
Summary of WASH-attributable disease burden, 2016
disease PAF 95% CI method for PAF estimation counterfactual exposure level deaths DALYs
Schistosomiasis 0.43 0.40–0.46 CRA feasible minimum risk (universal access to/use of basic water and sanitation services) 10,405 1,095,658
total WASH-attributable disease burden using a feasible minimum risk 10,405 1,095,658
Diarrhoea 0.60* 0.54–0.65 CRA plausible minimum risk (universal filtering/boiling of water + safe storage. access to/use of basic
sanitation in communities > 75% basic sanitation coverage, HWWS after potential faecal contact)
828,651* 49,773,959*
Acute respiratory infections 0.13 0.08–0.16 CRA plausible minimum risk (universal HWWS after potential faecal contact) 370,370 17,308,136
Protein-energy malnutrition 0.16* 0.15–0.17 based on diarrhoeal estimates plausible minimum risk (see diarrhoea) 28,194* 2,995,329*
total WASH-attributable disease burden using a plausible minimum risk 1,227,215 70,077,424
Malaria 0.80 0.67–0.87 comparing universal safe water resource
management (WRM) against no WRM
theoretical minimum risk (universal safe WRM) 354,924 29,707,805
Soil-transmitted helminth
infections
1 1–1 burden completely WASH-attributed theoretical minimum risk (universal safely managed water and sanitation, access to essential hygiene
conditions and practice of essential hygiene behaviours)
6248 3,430,614
Trachoma 1 1–1 burden completely WASH-attributed theoretical minimum risk (universal safely managed water and sanitation, access to essential hygiene
conditions and practice of essential hygiene behaviours)
< 10 244,471
total WASH-attributable disease burden using a theoretical risk 361,175 33,382,890
PAF: population attributable fraction, CI: confidence interval, DALYs: disability-adjusted life years, CRA: comparative risk assessment, HWWS: handwashing with soap, theoretical minimum risk: use of safely managed
water and sanitation services, access to essential hygiene conditions and practice of essential hygiene behaviour, plausible minimum risk: boiling/filtering of drinking water with subsequent safe storage, access to/use of
basic sanitation in a community with > 75% basic sanitation coverage, handwashing with soap after potential faecal contact, feasible minimum risk: access to/use of basic drinking water and basic sanitation services,
disease burden estimates are for low- and middle-income countries, diarrhoea and acute respiratory infections include disease burden in high-income countries from inadequate hygiene.
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
10
interventions. We do adjust the diarrhoeal disease burden estimates for
the likely overestimation of health impacts due to non-blinding by
adjusting the results of each non-blinded point-of-use drinking water
and hygiene intervention (Wolf et al., 2018a,2014). This approach
down-weights biased studies and – in our case – results in reduced es-
timated health impacts. The above cited issues on poor WASH inter-
ventions are however likely to underestimate the disease burden attri-
butable to inadequate WASH. This is one more reason why our
assessment assures conservative estimates which are at the lower end of
the assumed truth. The WASH-attributable disease burden estimates
from diarrhoea, soil-transmitted helminth infections and protein-energy
malnutrition have undergone country consultations which ensure the
use of all available and eligible exposure and disease data and com-
patible data categories.
The formula combining disease burden estimates from water, sa-
nitation and hygiene (eq. (2)) assumes that risk factors are in-
dependent (Steenland and Armstrong, 2006). This assumption is likely
to be an oversimplification for WASH as, for instance, handwashing
promotion is unlikely to be effective if water quantity is limited.
However, this approach has been applied in the assessment for ease of
interpretation of the results, and in the absence of a more suitable
approach.
WASH-attributable morbidity for some diseases in our analysis
(diarrhoea, schistosomiasis) is estimated separately for the different
components of WASH (water, sanitation and hygiene are analysed in
three separate models). This approach ignores that the different WASH
components affect disease in conjunction. The meta-regression model
(Wolf et al., 2018a) that was used to generate the exposure-response
relationships between WASH and diarrhoea, however adjusted for
baseline WASH of the other categories and included further covariates.
A multi-risk model might nevertheless be the preferred approach for
WASH-attributable disease burden assessment in the future. Including
all three WASH components in one model would also take account of
the fact that the three risk factors (inadequate water, inadequate sani-
tation and inadequate hygiene) are often likely to vary simultaneously,
e.g. improving access to or use of water facilities might improve hy-
giene behaviours and sanitation at the same time.
The here presented WASH-attributable burden of disease estimates
required different assumptions. We show through different sensitivity
analyses that disease burden estimates can change by as much as a
factor of two depending on assumptions, applied exposure-response
relationships and counterfactual definitions. Especially the WASH-at-
tributable schistosomiasis disease burden estimates, generated using
the feasible minimum risk exposure distribution, are likely to be un-
derestimated. Accordingly, estimates based on expert survey were
considerably higher. Care should be taken to consider the approximate
nature of the estimates which are however suitable to gauge the size of
the problem, to compare the relative importance of diseases and risk
factors and to monitor changes over time.
The attributable burden signifies the reduction in current or future
disease burden if past exposure to a risk factor had been equal to the
counterfactual exposure distribution (Murray et al., 2003). An as-
sumption that is made when stating the PAF is that the formerly ex-
posed group immediately attains disease risk of the unexposed group
after removal or reduction of the exposure (Kowall and Stang, 2018;
Rockhill et al., 1998). This is often not the case and additionally differs
between different health outcomes. For example, diarrhoea disease
reduction is likely to happen more immediate than changes in nutri-
tional status, universal water resource management may take a con-
siderable time to implement but once it is established disruption of
mosquito habitats will probably follow quite quickly. These different
time lags that are not apparent from the PAF need to be considered and
are important for interpreting results, prevention efforts, research and
policy.
5. Conclusions
An important fraction of overall deaths and DALYs in low- and
middle-income countries is attributable to inadequate WASH. Burden of
disease estimates have an approximate nature as they do not capture
the complete list of WASH-attributable adverse health outcomes, ex-
posed settings and populations and are dependent on assumptions,
exposure-response functions and chosen counterfactual definitions that
are often still based on imperfect WASH interventions.
To improve estimates of health benefits from WASH there is a need
for well-designed trials that evaluate the effectiveness of safely man-
aged water and sanitation services, access to essential hygiene condi-
tions and practice of essential hygiene behaviours that reach high
coverage and use in the communities. To improve health outcomes
there is a strong need for research on implementation systems, inter-
vention quality and intermediate outcomes such as exposure to faecal
pathogens in the community. Additionally, data from high-income
countries on WASH exposure distributions and exposure-response re-
lationships might strengthen future definitions of the theoretical
minimum exposure distribution and might enable more comprehensive
WASH disease burden assessments.
Acknowledgments and disclaimer
The study was partially funded by the United Kingdom Department
for International Development (DFID). The funder had no role in study
design, data collection and analysis, decision to publish, or preparation
of the manuscript. Some authors are staff members of the World Health
Organization or other institutions. The authors alone are responsible for
the views expressed in this publication, which do not necessarily re-
present the views, decisions or policies of the institutions with which
they are affiliated. This article should not be reproduced for use in
association with the promotion of commercial products, services or any
legal entity. The WHO does not endorse any specific organization or
products.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.ijheh.2019.05.004.
References
Alsan, M., Goldin, C., 2018. Watersheds in Infant Mortality: the Role of Effective Water
and Sewerage Infrastructure (No. 21263). NBER Working Paper Series.
Anuar, T.S., Salleh, F.M., Moktar, N., 2014. Soil-transmitted helminth infections and as-
sociated risk factors in three orang asli tribes in peninsular Malaysia. Sci. Rep. 4,
4101. https://doi.org/10.1038/srep04101.
Bain, R., Cronk, R., Wright, J., Yang, H., Slaymaker, T., Bartram, J., 2014. Fecal con-
tamination of drinking-water in low- and middle-income countries: a systematic re-
view and meta-analysis. PLoS Med. 11, e1001644. https://doi.org/10.1371/journal.
pmed.1001644.
Baum, R., Luh, J., Bartram, J., 2013. Sanitation: a global estimate of sewerage connec-
tions without treatment and the resulting impact on MDG progress. Environ. Sci.
Technol. 47, 1994–2000.
Bell, F., Millward, R., 1998. Public health expenditures and mortality in England and
Wales, 1870–1914. Continuity Change 13, 221–249.
Bürgmann, H., Frigon, D., H Gaze, W., M Manaia, C., Pruden, A., Singer, A.C., F Smets, B.,
Zhang, T., 2018. Water and sanitation: an essential battlefront in the war on anti-
microbial resistance. FEMS Microbiol. Ecol. 94. https://doi.org/10.1093/femsec/
fiy101.
Checkley, W., Buckley, G., Gilman, R.H., Assis, A.M., Guerrant, R.L., Morris, S.S., Mølbak,
K., Valentiner-Branth, P., Lanata, C.F., Black, R.E., 2008. Multi-country analysis of
the effects of diarrhoea on childhood stunting. Int. J. Epidemiol. 37, 816–830.
https://doi.org/10.1093/ije/dyn099.
Clasen, T., Prüss-Ustün, A., Mathers, C., Cumming, O., Cairncross, S., Colford Jr., J.M.,
2014. Estimating the impact of inadequate water, sanitation and hygiene on the
global burden of disease: evolving and alternative methods. J. Trop. Med. Int. Health.
Cutler, D., Deaton, A., Lleras-Muney, A., 2006. The determinants of mortality. J. Econ.
Perspect. 20, 97–120.
Dangour, A.D., Watson, L., Cumming, O., Boisson, S., Che, Y., Velleman, Y., Cavill, S.,
Allen, E., Uauy, R., 2013. Interventions to improve water quality and supply,
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
11
sanitation and hygiene practices, and their effects on the nutritional status of chil-
dren. Cochrane Database Syst. Rev. 8.
Ezzati, M., Lopez, A.D., Rodgers, A., Vander Hoorn, S., Murray, C.J., the Comparative Risk
Assessment Collaborating Group, 2002. Selected major risk factors and global and
regional burden of disease. Lancet 360, 1347–1360.
Fisher, M.B., Williams, A.R., Jalloh, M.F., Saquee, G., Bain, R.E., Bartram, J.K., 2015.
Microbiological and chemical quality of packaged sachet water and household stored
drinking water in Freetown, Sierra Leone. PLoS One 10, e0131772.
Forouzanfar, M.H., Alexander, L., Anderson, H.R., Bachman, V.F., Biryukov, S., Brauer,
M., Burnett, R., Casey, D., Coates, M.M., Cohen, A., others, 2015. Global, regional,
and national comparative risk assessment of 79 behavioural, environmental and
occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a
systematic analysis for the Global Burden of Disease Study 2013. Lancet 386,
2287–2323.
Forouzanfar, M.H., Afshin, A., Alexander, L.T., Anderson, H.R., Bhutta, Z.A., Biryukov, S.,
Brauer, M., Burnett, R., Cercy, K., Charlson, F.J., others, 2016. Global, regional, and
national comparative risk assessment of 79 behavioural, environmental and occu-
pational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for
the Global Burden of Disease Study 2015. Lancet 388, 1659–1724.
Freeman, M.C., Garn, J.V., Sclar, G.D., Boisson, S., Medlicott, K., Alexander, K.T.,
Penakalapati, G., Anderson, D., Mahtani, A.G., Grimes, J.E., 2017. The impact of
sanitation on infectious disease and nutritional status: a systematic review and meta-
analysis. Int. J. Hyg Environ. Health 220, 928–949.
Fuller, J.A., Eisenberg, J.N., 2016. Herd protection from drinking water, sanitation, and
hygiene interventions. Am. J. Trop. Med. Hyg. 95, 1201–1210.
Gakidou, E., Afshin, A., Abajobir, A.A., Abate, K.H., Abbafati, C., Abbas, K.M., Abd-Allah,
F., Abdulle, A.M., Abera, S.F., Aboyans, V., 2017. Global, regional, and national
comparative risk assessment of 84 behavioural, environmental and occupational, and
metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global
Burden of Disease Study 2016. Lancet 390, 1345–1422.
GATHER Guidelines for Accurate and Transparent Health Estimates Reporting. [WWW
Document], n.d. . Gather Statement. http://gather-statement.org/ accessed 4.16.18.
Greenland, S., 2002. Causality theory for policy uses of epidemiological measures. In:
Summary Measures of Population Health: Concepts, Ethics, Measurement, and
Application. World Health Organization, Geneva.
Grimes, J.E., Croll, D., Harrison, W.E., Utzinger, J., Freeman, M.C., Templeton, M.R.,
2014. The relationship between water, sanitation and schistosomiasis: a systematic
review and meta-analysis. PLoS Neglected Trop. Dis. 8, e3296.
Gunnarsdottir, M.J., Gardarsson, S.M., Elliott, M., Sigmundsdottir, G., Bartram, J., 2012.
Benefits of water safety plans: microbiology, compliance, and public health. Environ.
Sci. Technol. 46, 7782–7789.
Hu, V.H., Harding-Esch, E.M., Burton, M.J., Bailey, R.L., Kadimpeul, J., Mabey, D.C.W.,
2010. Epidemiology and control of trachoma: systematic review. Trop. Med. Int.
Health 15, 673–691. https://doi.org/10.1111/j.1365-3156.2010.02521.x.
Humphrey, J.H., Mbuya, M.N., Nitozini, R., Moulton, L.H., Stoltzfus, R.J., Tavengwa,
N.V., Mutasa, K., Majo, F., Mutasa, B., Mangwadu, G., Chasokela, C.M., Chigumira,
A., Chasekwa, B., Smith, L.E., Tielsch, J.M., Jones, A.D., Manges, A., Maluccio, J.A.,
Prendergast, A., 2019. Independent and combined effects of improved water, sani-
tation, and hygiene, and improved complementary feeding, on child stunting and
anaemia in rural Zimbabwe: a cluster-randomised trial. Lancet Glob. Health 7,
e132–e147.
Jung, Y.T., Hum, R.J., Lou, W., Cheng, Y.-L., 2017a. Effects of neighbourhood and
household sanitation conditions on diarrhea morbidity: systematic review and meta-
analysis. PLoS One 12, e0173808. https://doi.org/10.1371/journal.pone.0173808.
Jung, Y.T., Lou, W., Cheng, Y.-L., 2017b. Exposure-response relationship of neighbour-
hood sanitation and children's diarrhea. Trop. Med. Int. Health 22, 857–865.
Keiser, J., Singer, B.H., Utzinger, J., 2005. Reducing the burden of malaria in different
eco-epidemiological settings with environmental management: a systematic review.
Lancet Infect. Dis. 5, 695–708. https://doi.org/10.1016/S1473-3099(05)70268-1.
Kotloff, K.L., Nasrin, D., Blackwelder, W.C., Wu, Y., Farag, T., Panchalingham, S., Sow,
S.O., Sur, D., Zaidi, A.K.M., Faruque, A.S.G., Saha, D., Alonso, P.L., Tamboura, B.,
Sanogo, D., Onwuchekwa, U., Manna, B., Ramamurthy, T., Kanungo, S., Ahmed, S.,
Qureshi, S., Quadri, F., Hossain, A., Das, S.K., Antonio, M., Hossain, M.J.,
Mandomando, I., Acácio, S., Biswas, K., Tennant, S.M., Verweij, J.J., Sommerfelt, H.,
Nataro, J.P., Robins-Browne, R.M., Levine, M.M., 2019. The incidence, aetiology, and
adverse clinical consequences of less severe diarrhoeal episodes among infants and
children residing in low-income and middle-income countries: a 12-month case-
control study as a follow-on to the Global Enteric Multicenter Study (GEMS). Lancet
Glob. Health 7, e568–e584. https://doi.org/10.1016/S2214-109X(19)30076-2.
Kowall, B., Stang, A., 2018. Stolpersteine bei der Interpretation des populationsat-
tributablen Risikos. Gesundheitswesen 80, 149–153.
Lim, S.S., Vos, T., Flaxman, A.D., Danaei, G., Shibuya, K., Adair-Rohani, H., Amann, M.,
Anderson, H.R., Andrews, K.G., Aryee, M., 2012. A comparative risk assessment of
burden of disease and injury attributable to 67 risk factors and risk factor clusters in
21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study
2010. Lancet 380, 2224–2260.
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., Das, K.K.,
Abedin, J., Clasen, T.F., Dewey, K.G., Fernald, L.C., Null, C., Ahmed, T., Colford, J.M.,
2018. Effects of water quality, sanitation, handwashing, and nutritional interventions
on diarrhoea and child growth in rural Bangladesh: a cluster randomised controlled
trial. Lancet Glob. Health 6, e302–e315. https://doi.org/10.1016/S2214-109X(17)
30490-4.
MAL-ED Network Investigators, 2017. Relationship between growth and illness, en-
teropathogens and dietary intakes in the first 2 years of life: findings from the MAL-
ED birth cohort study. BMJ Glob. Health 2, e000370. https://doi.org/10.1136/
bmjgh-2017-000370.
Mbakaya, B.C., Lee, P.H., Lee, R.L., 2017. Hand hygiene intervention strategies to reduce
diarrhoea and respiratory infections among schoolchildren in developing countries: a
systematic review. Int. J. Environ. Res. Public Health 14, 371.
Mohammadpour, M., Abrishami, M., Masoumi, A., Hashemi, H., 2016. Trachoma: past,
present and future. J. Curr. Ophthalmol. 28, 165–169. https://doi.org/10.1016/j.
joco.2016.08.011.
Murray, C.J.L., Lopez, A.D., 1996. The Global Burden of Disease. World Health
Organization. Harvard School of Public Health, World Bank, Geneva.
Murray, C.J.L., Lopez, A.D., 2013. Measuring the global burden of disease. N. Engl. J.
Med. 369, 448–457. https://doi.org/10.1056/NEJMra1201534.
Murray, C.J., Ezzati, M., Lopez, A.D., Rodgers, A., Vander Hoorn, S., 2003. Comparative
quantification of health risks: conceptual framework and methodological issues.
Popul. Health Metrics 1, 1.
Myatt, M., Khara, T., Schoenbuchner, S., Pietzsch, S., Dolan, C., Lelijveld, N., Briend, A.,
2018. Children who are both wasted and stunted are also underweight and have a
high risk of death: a descriptive epidemiology of multiple anthropometric deficits
using data from 51 countries. Arch. Public Health 76. https://doi.org/10.1186/
s13690-018-0277-1.
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., 2018. Effects of water quality, sa-
nitation, handwashing, and nutritional interventions on diarrhoea and child growth
in rural Kenya: a cluster-randomised controlled trial. Lancet Glob. Health 6,
e316–e329.
O'Neill, J., 2016. Tackling Drug-Resistant Infections Globally: Final Report and
Recommendations, the Review on Antimicrobial Resistance. Wellcome Trust. HM
Government.
Prüss-Ustün, A., Bos, R., Gore, F., Bartram, J., 2008. Safer Water, Better Health. World
Health Organization, Geneva.
Prüss-Ustün, A., Bartram, J., Clasen, T., Colford Jr., J.M., Cumming, O., Curtis, V.,
Bonjour, S., Dangour, A.D., De France, J., Fewtrell, L., Freeman, M.C., Gordon, B.,
Hunter, P.R., Johnston, R.B., Mathers, C., Mäusezahl, D., Medlicott, K., Neira, M.,
Stocks, M., Wolf, J., Cairncross, S., 2014. Burden of disease from inadequate water,
sanitation and hygiene in low- and middle-income settings: a retrospective analysis of
data from 145 countries. Trop. Med. Int. Health 19, 894–905. https://doi.org/10.
1111/tmi.12329.
Prüss-Ustün, A., Wolf, J., Corvalán, C., Bos, R., Neira, M., 2016. Preventing Disease
through Healthy Environments: A Global Assessment of the Environmental Burden of
Disease from Environmental Risks. World Health Organization.
Rabie, T., Curtis, V., 2006. Handwashing and risk of respiratory infections: a quantitative
systematic review. Trop. Med. Int. Health TM IH 11, 258–267. https://doi.org/10.
1111/j.1365-3156.2006.01568.x.
Reese, H., Routray, P., Torondel, B., Sclar, G.D., Delea, M., Sinharoy, S., Zambrano, L.,
Caruso, B., Suar, M., Clasen, T., 2018. Effect of a Combined Household-Level Piped
Water and Sanitation Intervention in Rural Odisha, India on Diarrheal Diseases,
Respiratory Infection. soil-transmitted helminth infection, and undernutrition.
RISK, (n.d.), https://www.palisade.com/risk/de/.
Rockhill, B., Newman, B., Weinberg, C., 1998. Use and misuse of population attributable
fractions. Am. J. Public Health 88, 15–19.
Rogawski, E.T., Guerrant, R.L., 2017. The burden of enteropathy and “subclinical” in-
fections. Pediatr. Clin. 64, 815–836. https://doi.org/10.1016/j.pcl.2017.03.003.
Savović, J., Jones, H.E., Altman, D.G., Harris, R.J., Jüni, P., Pildal, J., Als-Nielsen, B.,
Balk, E.M., Gluud, C., Gluud, L.L., 2012. Influence of reported study design char-
acteristics on intervention effect estimates from randomized, controlled trials. Ann.
Intern. Med. 157, 429–438.
Schnee, A.E., Haque, R., Taniuchi, M., Uddin, M.J., Alam, M.M., Liu, J., Rogawski, E.T.,
Kirkpatrick, B., Houpt, E.R., Petri Jr., W.A., 2018. Identification of etiology-specific
diarrhea associated with linear growth faltering in Bangladeshi infants. Am. J.
Epidemiol. 187, 2210–2218.
Setty, K.E., Kayser, G.L., Bowling, M., Enault, J., Loret, J.-F., Serra, C.P., Alonso, J.M.,
Mateu, A.P., Bartram, J., 2017. Water quality, compliance, and health outcomes
among utilities implementing water safety plans in France and Spain. Int. J. Hyg.
Environ. Health, special issue: eighth PhD students workshop: water and health –.
Cannes 220, 513–530. 2016. https://doi.org/10.1016/j.ijheh.2017.02.004.
Sima, L.C., Desai, M.M., McCarty, K.M., Elimelech, M., 2012. Relationship between use of
water from community-scale water treatment refill kiosks and childhood diarrhea in
jakarta. Am. J. Trop. Med. Hyg. 87, 979–984. https://doi.org/10.4269/ajtmh.2012.
12-0224.
Stanaway, J.D., Afshin, A., Gakidou, E., Lim, S.S., Abate, D., Abate, K.H., Abbafati, C.,
Abbasi, N., Abbastabar, H., Abd-Allah, F., 2018. Global, regional, and national
comparative risk assessment of 84 behavioural, environmental and occupational, and
metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a
systematic analysis for the Global Burden of Disease Study 2017. Lancet 392,
1923–1994.
statista, 2016. Bottled Water Consumption Worldwide from 2007 to 2017 (In Billion
Liters). [WWW Document]. Statista. http://www.statista.com/statistics/387255/
global-bottled-water-consumption/ accessed 7.28.16.
Steenland, K., Armstrong, B., 2006. An overview of methods for calculating the burden of
disease due to specific risk factors. Epidemiology 512–519.
Stevens, G.A., Alkema, L., Black, R.E., Boerma, J.T., Collins, G.S., Ezzati, M., Grove, J.T.,
Hogan, D.R., Hogan, M.C., Horton, R., 2016. Guidelines for accurate and transparent
health estimates reporting: the GATHER statement. PLoS Med. 13, e1002056.
Stocks, M.E., Ogden, S., Haddad, D., Addiss, D.G., McGuire, C., Freeman, M.C., 2014.
Effect of water, sanitation, and hygiene on the prevention of trachoma: a systematic
review and meta-analysis. PLoS Med. 11, e1001605.
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
12
Strunz, E.C., Addiss, D.G., Stocks, M.E., Ogden, S., Utzinger, J., Freeman, M.C., 2014.
Water, sanitation, hygiene, and soil-transmitted helminth infection: a systematic re-
view and meta-analysis. PLoS Med. 11, e1001620.
UNICEF, WHO, 2015. 25 Years Progress on Sanitation and Drinking Water, 2015 Update
and MDG Assessment. UNICEF and World Health Organization, Geneva, Switzerland.
United Nations, 2018. UN Stats - SDG Indicators. [WWW Document]. https://unstats.un.
org/sdgs/metadata/ accessed 6.25.18.
Vander Hoorn, S., Ezzati, M., Rodgers, A., Lopez, A.D., Murray, C.J.L., 2004. Chapter 25:
estimating attributable burden of disease from exposure and hazard data. In:
Comparative Quantification of Health Risks. World Health Organization, Geneva, pp.
2129–2140.
Warren‐Gash, C., Fragaszy, E., Hayward, A.C., 2013. Hand hygiene to reduce community
transmission of influenza and acute respiratory tract infection: a systematic review.
Influenza Other Respir. Viruses 7, 738–749. https://doi.org/10.1111/irv.12015.
White, C.G., Shinder, F.S., Shinder, A.L., Dyer, D.L., 2001. Reduction of illness ab-
senteeism in elementary schools using an alcohol-free instant hand sanitizer. J. Sch.
Nurs. 17, 248–265.
WHO, 2002. The World Health Report 2002 - Reducing Risks, Promoting Healthy Life.
World Health Organization, Geneva, Switzerland.
WHO, 2004. Comparative Quantification of Health Risks. World Health Organization,
Geneva.
WHO, 2014. Antimicrobial Resistance: an Emerging Water, Sanitation and Hygiene Issue
(Briefing Note). World Health Organization.
WHO, 2017a. WHO regional groupings. In: World Health Statistics 2017: Monitoring
Health for the SDGs. Sustainable Development Goals, pp. 103.
WHO, 2017b. Diarrhoeal Disease: Fact Sheet. [WWW Document]. https://www.who.int/
news-room/fact-sheets/detail/diarrhoeal-disease accessed 4.29.19.
WHO, 2018a. Global Health Observatory (GHO) Data. WHO [WWW Document]. http://
www.who.int/gho/en/ accessed 6.25.18.
WHO, 2018b. Disease Burden and Mortality Estimates. WHO [WWW Document]. http://
www.who.int/healthinfo/global_burden_disease/estimates/en/ accessed 11.21.18.
WHO, 2018c. Schistosomiasis: Fact Sheet. [WWW Document]. http://www.who.int/
news-room/fact-sheets/detail/schistosomiasis accessed 7.24.18.
WHO, 2018d. Soil-transmitted Helminth Infections: Fact Sheet. [WWW Document].
http://www.who.int/news-room/fact-sheets/detail/soil-transmitted-helminth-
infections accessed 7.24.18.
WHO, 2018e. Trachoma: Fact Sheet. [WWW Document]. http://www.who.int/news-
room/fact-sheets/detail/trachoma accessed 7.24.18.
WHO, 2018f. Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country and
by Region, 2000-2016. World Health Organization, Geneva.
WHO, 2018g. Antimicrobial Resistance: Fact Sheet. World Health Organ [WWW
Document]. http://www.who.int/news-room/fact-sheets/detail/antimicrobial-
resistance accessed 8.29.18.
WHO, 2018h. Malnutrition: Fact Sheet. World Health Organ [WWW Document]. http://
www.who.int/news-room/fact-sheets/detail/malnutrition accessed 9.14.18.
WHO, 2018i. WHO Methods and Data Sources for Country-Level Causes of Death 2000-
2016 (No. WHO/HIS/IER/GHE/2018.3), Global Health Estimates Technical Paper.
World Health Organization, Geneva.
WHO, IWA, 2017. Global Status Report on Water Safety Plans: A Review of Proactive Risk
Assessment and Risk Management Practices to Ensure the Safety of Drinking-Water.
World Health Organization, International Water Association.
WHOUNICEF Undated. Home | JMP. [WWW Document]. https://washdata.org/ accessed
6.3.18.
WHO, UNICEF, 2017. Progress on Drinking Water, Sanitation and Hygiene: 2017 Update
and SDG Baselines. WHO, UNICEF, Geneva.
WHO, UNICEF, 2018a. Monitoring: Hygiene. JMP [WWW Document]. https://washdata.
org/monitoring/hygiene accessed 6.7.18.
WHO, UNICEF, 2018b. Data. JMP [WWW Document]. https://washdata.org/data ac-
cessed 5.18.18.
Williams, A.R., Bain, R.E.S., Fisher, M.B., Cronk, R., Kelly, E.R., Bartram, J., 2015. A
systematic review and meta-analysis of fecal contamination and inadequate treat-
ment of packaged water. PLoS One 10, e0140899. https://doi.org/10.1371/journal.
pone.0140899.
Wolf, J., Bonjour, S., Prüss-Ustün, A., 2013. An exploration of multilevel modeling for
estimating access to drinking-water and sanitation. J. Water Health 11, 64. https://
doi.org/10.2166/wh.2012.107.
Wolf, J., Prüss-Ustün, A., Cumming, O., Bartram, J., Bonjour, S., Cairncross, S., Clasen, T.,
Colford Jr., J.M., Curtis, V., De France, J., Fewtrell, L., Freeman, M.C., Gordon, B.,
Hunter, P.R., Jeandron, A., Johnston, R.B., Mäusezahl, D., Mathers, C., Neira, M.,
Higgins, J., 2014. Assessing the impact of drinking-water and sanitation on diar-
rhoeal disease in low-and middle-income settings: a systematic review and meta-
regression. Trop. Med. Int. Health 19, 928–942. https://doi.org/0.1111/tmi.12331.
Wolf, J., Hunter, P.R., Freeman, M.C., Cumming, O., Clasen, T., Bartram, J., Higgins,
J.P.T., Johnston, R., Medlicott, K., Boisson, S., Prüss-Ustün, A., 2018a. Impact of
drinking water, sanitation and hand washing with soap on childhood diarrhoeal
disease: updated meta-analysis and –regression. Trop. Med. Int. Health 23. https://
doi.org/10.1111/tmi.13051.
Wolf, J., Johnston, R., Freeman, M.C., Ram, P.K., Slaymaker, T., Laurenz, E., Prüss-Ustün,
A., 2018b. Handwashing with soap after potential faecal contact: global, regional and
country estimates for handwashing with soap after potential faecal contact. Int. J.
Epidemiol. https://doi.org/dyy253.
Wolf, J., Johnston, R., Hunter, P.R., Gordon, B., Medlicott, K.O., Prüss-Ustün, A., 2018c. A
Faecal Contamination Index for interpreting heterogeneous diarrhoea impacts of
water, sanitation and hygiene interventions and overall, regional and country esti-
mates of community sanitation coverage with a focus on low- and middle-income
countries. Int. J. Hyg Environ. Health (in press), corrected proof.
Wood, L., Egger, M., Gluud, L.L., Schulz, K.F., Jüni, P., Altman, D.G., Gluud, C., Martin,
R.M., Wood, A.J., Sterne, J.A., 2008. Empirical evidence of bias in treatment effect
estimates in controlled trials with different interventions and outcomes: meta-epi-
demiological study. BMJ 336, 601.
World Bank, 2016. New Country Classifications by Income Level: 2016-2017. Data Blog
[WWW Document]. http://blogs.worldbank.org/opendata/miga/new-country-
classifications-2016 accessed 8.27.18.
Wright, J., Dzodzomenyo, M., Wardrop, N.A., Johnston, R., Hill, A., Aryeetey, G., Adanu,
R., 2016. Effects of sachet water consumption on exposure to microbe-contaminated
drinking water: household survey evidence from Ghana. Int. J. Environ. Res. Public
Health 13, 303. https://doi.org/10.3390/ijerph13030303.
A. Prüss-Ustün, et al. International Journal of Hygiene and Environmental Health xxx (xxxx) xxx–xxx
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Summary Background Child stunting reduces survival and impairs neurodevelopment. We tested the independent and combined effects of improved water, sanitation, and hygiene (WASH), and improved infant and young child feeding (IYCF) on stunting and anaemia in in Zimbabwe. Methods We did a cluster-randomised, community-based, 2 × 2 factorial trial in two rural districts in Zimbabwe. Clusters were defined as the catchment area of between one and four village health workers employed by the Zimbabwe Ministry of Health and Child Care. Women were eligible for inclusion if they permanently lived in clusters and were confirmed pregnant. Clusters were randomly assigned (1:1:1:1) to standard of care (52 clusters), IYCF (20 g of a small-quantity lipid-based nutrient supplement per day from age 6 to 18 months plus complementary feeding counselling; 53 clusters), WASH (construction of a ventilated improved pit latrine, provision of two handwashing stations, liquid soap, chlorine, and play space plus hygiene counselling; 53 clusters), or IYCF plus WASH (53 clusters). A constrained randomisation technique was used to achieve balance across the groups for 14 variables related to geography, demography, water access, and community-level sanitation coverage. Masking of participants and fieldworkers was not possible. The primary outcomes were infant length-for-age Z score and haemoglobin concentrations at 18 months of age among children born to mothers who were HIV negative during pregnancy. These outcomes were analysed in the intention-to-treat population. We estimated the effects of the interventions by comparing the two IYCF groups with the two non-IYCF groups and the two WASH groups with the two non-WASH groups, except for outcomes that had an important statistical interaction between the interventions. This trial is registered with ClinicalTrials.gov, number NCT01824940. Findings Between Nov 22, 2012, and March 27, 2015, 5280 pregnant women were enrolled from 211 clusters. 3686 children born to HIV-negative mothers were assessed at age 18 months (884 in the standard of care group from 52 clusters, 893 in the IYCF group from 53 clusters, 918 in the WASH group from 53 clusters, and 991 in the IYCF plus WASH group from 51 clusters). In the IYCF intervention groups, the mean length-for-age Z score was 0·16 (95% CI 0·08–0·23) higher and the mean haemoglobin concentration was 2·03 g/L (1·28–2·79) higher than those in the non-IYCF intervention groups. The IYCF intervention reduced the number of stunted children from 620 (35%) of 1792 to 514 (27%) of 1879, and the number of children with anaemia from 245 (13·9%) of 1759 to 193 (10·5%) of 1845. The WASH intervention had no effect on either primary outcome. Neither intervention reduced the prevalence of diarrhoea at 12 or 18 months. No trial-related serious adverse events, and only three trial-related adverse events, were reported. Interpretation Household-level elementary WASH interventions implemented in rural areas in low-income countries are unlikely to reduce stunting or anaemia and might not reduce diarrhoea. Implementation of these WASH interventions in combination with IYCF interventions is unlikely to reduce stunting or anaemia more than implementation of IYCF alone. Funding Bill & Melinda Gates Foundation, UK Department for International Development, Wellcome Trust, Swiss Development Cooperation, UNICEF, and US National Institutes of Health.
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Background Limited data have been available on the global practice of handwashing with soap (HWWS). To better appreciate global HWWS frequency, which plays a role in disease transmission, our objectives were to: (i) quantify the presence of designated handwashing facilities; (ii) assess the association between handwashing facility presence and observed HWWS; and (iii) derive country, regional and global HWWS estimates after potential faecal contact. Methods First, using data from national surveys, we applied multilevel linear modelling to estimate national handwashing facility presence. Second, using multilevel Poisson modelling on datasets including both handwashing facility presence and observed HWWS after potential faecal contact, we estimated HWWS prevalence conditional on handwashing facility presence by region. For high-income countries, we used meta-analysis to pool handwashing prevalence of studies identified through a systematic review. Third, from the modelled handwashing facility presence and estimated HWWS prevalence conditional on the presence of a handwashing facility, we estimated handwashing practice at country, regional and global levels. Results First, approximately one in four persons did not have a designated handwashing facility in 2015, based on 115 data points for 77 countries. Second the prevalence ratio between HWWS when a designated facility was present compared with when it was absent was 1.99 (1.66, 2.39) P <0.001 for low- and middle-income countries, based on nine datasets. Third, we estimate that in 2015, 26.2% (23.1%, 29.6%) of potential faecal contacts were followed by HWWS. Conclusions Many people lack a designated handwashing facility, but even among those with access, HWWS is poorly practised. People with access to designated handwashing facilities are about twice as likely to wash their hands with soap after potential faecal contact as people who lack a facility. Estimates are based on limited data.
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Objectives: The impact on diarrhoea of sanitation interventions has been heterogeneous. We hypothesize that this is due to the level of prevailing faecal environmental contamination and propose a Faecal Contamination Index (FAECI) of selected WASH indicators (objective 1). Additionally, we provide estimates of the proportion of the population living in communities above certain sanitation coverage levels (objective 2). Methods: Objective 1: Faecal contamination post-intervention was estimated from WASH intervention reports. WASH indicators composing the FAECI included eight water, sanitation and hygiene practice indicators, which were selected for their relevance for health and data availability at study- and country-level. The association between the estimated level of faecal environmental contamination and diarrhoea was examined using meta-regression. Objective 2: A literature search was conducted to identify health-relevant community sanitation coverage thresholds. To estimate total community coverage with basic sanitation in low- and middle-income countries, at relevant thresholds, household surveys with data available at primary sampling unit (PSU)-level were analysed according to the identified thresholds, at country-, regional- and overall level. Results: Objective 1: We found a non-linear association between estimated environmental faecal contamination and sanitation interventions' impact on diarrhoeal disease. Diarrhoea reductions were highest at lower faecal contamination levels, and no diarrhoea reduction was found when contamination increased above a certain level. Objective 2: Around 45% of the population lives in communities with more than 75% of coverage with basic sanitation and 24% of the population lives in communities above 95% coverage, respectively. Conclusions: High prevailing faecal contamination might explain interventions' poor effectiveness in reducing diarrhoea. The here proposed Faecal Contamination Index is a first attempt to estimate the level of faecal contamination in communities. Much of the world's population currently lives in faecally contaminated environments as indicated by low community sanitation coverage.
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Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning.
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The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of risk factor exposure and attributable burden of disease. By providing estimates over a long time series, this study can monitor risk exposure trends critical to health surveillance and inform policy debates on the importance of addressing risks in context.We used the comparative risk assessment framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2016. This study included 481 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk (RR) and exposure estimates from 22 717 randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources, according to the GBD 2016 source counting methods. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. Finally, we explored four drivers of trends in attributable burden: population growth, population ageing, trends in risk exposure, and all other factors combined.Since 1990, exposure increased significantly for 30 risks, did not change significantly for four risks, and decreased significantly for 31 risks. Among risks that are leading causes of burden of disease, child growth failure and household air pollution showed the most significant declines, while metabolic risks, such as body-mass index and high fasting plasma glucose, showed significant increases. In 2016, at Level 3 of the hierarchy, the three leading risk factors in terms of attributable DALYs at the global level for men were smoking (124·1 million DALYs [95% UI 111·2 million to 137·0 million]), high systolic blood pressure (122·2 million DALYs [110·3 million to 133·3 million], and low birthweight and short gestation (83·0 million DALYs [78·3 million to 87·7 million]), and for women, were high systolic blood pressure (89·9 million DALYs [80·9 million to 98·2 million]), high body-mass index (64·8 million DALYs [44·4 million to 87·6 million]), and high fasting plasma glucose (63·8 million DALYs [53·2 million to 76·3 million]). In 2016 in 113 countries, the leading risk factor in terms of attributable DALYs was a metabolic risk factor. Smoking remained among the leading five risk factors for DALYs for 109 countries, while low birthweight and short gestation was the leading risk factor for DALYs in 38 countries, particularly in sub-Saharan Africa and South Asia. In terms of important drivers of change in trends of burden attributable to risk factors, between 2006 and 2016 exposure to risks explains an 9·3% (6·9-11·6) decline in deaths and a 10·8% (8·3-13·1) decrease in DALYs at the global level, while population ageing accounts for 14·9% (12·7-17·5) of deaths and 6·2% (3·9-8·7) of DALYs, and population growth for 12·4% (10·1-14·9) of deaths and 12·4% (10·1-14·9) of DALYs. The largest contribution of trends in risk exposure to disease burden is seen between ages 1 year and 4 years, where a decline of 27·3% (24·9-29·7) of the change in DALYs between 2006 and 2016 can be attributed to declines in exposure to risks.Increasingly detailed understanding of the trends in risk exposure and the RRs for each risk-outcome pair provide insights into both the magnitude of health loss attributable to risks and how modification of risk exposure has contributed to health trends. Metabolic risks warrant particular policy attention, due to their large contribution to global disease burden, increasing trends, and variable patterns across countries at the same level of development. GBD 2016 findings show that, while it has huge potential to improve health, risk modification has played a relatively small part in the past decade.The Bill & Melinda Gates Foundation, Bloomberg Philanthropies.
Article
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Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning.
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Abstract Background: Wasting and stunting are common. They are implicated in the deaths of almost two million children each year and account for over 12% of disability-adjusted life years lost in young children. Wasting and stunting tend to be addressed as separate issues despite evidence of common causality and the fact that children may suffer simultaneously from both conditions (WaSt). Questions remain regarding the risks associated with WaSt, which children are most affected, and how best to reach them. Methods: A database of cross-sectional survey datasets containing data for almost 1.8 million children was compiled. This was analysed to determine the intersection between sets of wasted, stunted, and underweight children; the association between being wasted and being stunted; the severity of wasting and stunting in WaSt children; the prevalence of WaSt by age and sex, and to identify weight-for-age z-score and mid-upper arm circumference thresholds for detecting cases of WaSt. An additional analysis of the WHO Growth Standards sought the maximum possible weight-for-age z-score for WaSt children. Results: All children who were simultaneously wasted and stunted were also underweight. The maximum possible weight-for-age z-score in these children was below − 2.35. Low WHZ and low HAZ have a joint effect on WAZ which varies with age and sex. WaSt and “multiple anthropometric deficits” (i.e. being simultaneously wasted, stunted, and underweight) are identical conditions. The conditions of being wasted and being stunted are positively associated with each other. WaSt cases have more severe wasting than wasted only cases. WaSt cases have more severe stunting than stunted only cases. WaSt is largely a disease of younger children and of males. Cases of WaSt can be detected with excellent sensitivity and good specificity using weight-for-age. Conclusions: The category “multiple anthropometric deficits” can be abandoned in favour of WaSt. Therapeutic feeding programs should cover WaSt cases given the high mortality risk associated with this condition. Work on treatment effectiveness, duration of treatment, and relapse after cure for WaSt cases should be undertaken. Routine reporting of the prevalence of WaSt should be encouraged. Further work on the aetiology, prevention, case-finding, and treatment of WaSt cases as well as the extent to which current interventions are reaching WaSt cases is required.
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Water and sanitation represents a key battlefront in combating the spread of antimicrobial resistance (AMR). Basic water sanitation infrastructure is an essential first step to protecting public health, thereby limiting the spread of pathogens and the need for antibiotics. AMR presents unique human health risks, meriting new risk assessment frameworks specifically adapted to water and sanitation-borne AMR. There are numerous exposure routes to AMR originating from human waste, each of which must be quantified for its relative risk to human health. Wastewater treatment plants (WWTPs) play a vital role in centralized collection and treatment of human sewage, but there are numerous unresolved questions in terms of the microbial ecological processes occurring within and the extent to which they attenuate or amplify AMR. Research is needed to advance understanding of the fate of resistant bacteria and antibiotic resistance genes (ARGs) in various waste management systems, depending on the local constraints and intended re-use applications. WHO and national AMR action plans would benefit from a more holistic 'One Water' understanding. Here we provide a framework for research, policy, practice, and public engagement aimed at limiting the spread of AMR from water and sanitation in both low-, medium- and high-income countries, alike.
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We explore the first period of sustained decline in child mortality in the United States and provide estimates of the independent and combined effects of clean water and effective sewerage systems on under-5 mortality. Our case is Massachusetts, 1880–1920, when authorities developed a sewerage and water district in the Boston area. We find the two interventions were complementary and together account for approximately one-third of the decline in log child mortality during the 41 years. Our findings are relevant to the developing world and suggest that a piecemeal approach to infrastructure investments is unlikely to significantly improve child health.