Please cite this article as: Giné Garriga R, Jiménez A, Pérez Foguet A. Water-sanitation-hygiene mapping:
An improved approach for data collection at local level. Sci. Total Environ. 2013;463-464:700-711
To link to this article: http://dx.doi.org/10.1016/j.scitotenv.2013.06.005
Water - Sanitation - Hygiene Mapping: an improved approach
for data collection at local level
R. Giné Garriga, A. Jiménez Fdez. de Palencia, A. Pérez Foguet
Strategic planning and appropriate development and management of water and sanitation services
are strongly supported by accurate and accessible data. If adequately exploited, these data might
assist water managers with performance monitoring, benchmarking comparisons, policy progress
evaluation, resources allocation, and decision making. A variety of tools and techniques are in
place to collect such information. However, some methodological weaknesses arise when
developing an instrument for routine data collection, particularly at local level: i) comparability
problems due to heterogeneity of indicators, ii) poor reliability of collected data, iii) inadequate
combination of different information sources, and iv) statistical validity of produced estimates
when disaggregated into small geographic subareas.
This study proposes an improved approach for water, sanitation and hygiene (WASH) data
collection at decentralised level in low income settings, as an attempt to overcome previous
shortcomings. The ultimate aim is to provide local policymakers with strong evidences to inform
their planning decisions. The survey design takes the Water Point Mapping (WPM) as a starting
point to record all available water sources at a particular location. This information is then linked
to data produced by a household survey. Different survey instruments are implemented to collect
reliable data by employing a variety of techniques, such as structured questionnaires, direct
observation and water quality testing. The collected data is finally validated through simple
statistical analysis, which in turn produces valuable outputs that might feed into the decision-
making process. In order to demonstrate the applicability of the method, outcomes produced from
three different case studies (Homa Bay District -Kenya-; Kibondo District -Tanzania-; and
Municipality of Manhiça –Mozambique-) are presented.
data collection, data management; water point mapping; household survey; WASH; East Africa;
JMP Joint Monitoring Programme for Water Supply and Sanitation
MICS Multiple Indicator Cluster Survey
UNICEF United Nations Children’s Fund
WASH Water, Sanitation and Hygiene
WPM Water Point Mapping
Water and sanitation improvements together with good hygiene (WASH) produce evident effects on
health population (Cairncross et al., 2010; Curtis and Cairncross, 2003; Esrey et al., 1991; Feachem,
1984; Fewtrell et al., 2005). However, universal access to safe drinking water and basic sanitation
remains a huge challenge in many low income countries (Joint Monitoring Programme, 2012a),
where vast numbers of people are not properly provided for by these basic services. To help end this
appalling state of affairs, the sector has been facing a gradual process of decentralisation, where the
responsibility in service provision moves to local authorities. It is believed that decentralised
governments have an informational advantage over the central government with regard to local
needs and priorities, for which reason they are assumed to supply services in accordance with
demand, allocate resources more equitably, and ultimately conceive and implement policies with a
focus on poverty reduction (Crook, 2003; Devas and Grant, 2003; Steiner, 2007). To effectively do
this, local governments need to make evidence-based decisions, which primarily depend on the
availability of accessible, accurate and reliable data that are routinely collected, disseminated and
updated. Amongst others, these data may be employed to i) measure progress and performance, ii)
improve transparency in budgetary procedures and promote increased investments in the sector, and
iii) allocate resources to deliver services where they are most needed. Today, reliable information
on key indicators at local level often lacks, but even when it is available, the uptake for such data by
policymakers is, at best, challenging (WaterAid, 2010). Limited capacities of recipient
governmental bodies, inadequate sector-related institutional framework, and lack of data updating
mechanisms are common reasons that hamper an adequate appropriation and continued use of the
data for planning and monitoring purposes (Joint Monitoring Programme, 2011; World Health
In an effort to address one of the shortcomings cited above, i.e. the lack of reliable data, this study
deals with the design of adequate methodologies for routine data collection. A variety of tools and
techniques have been developed in recent years to collect primary data for the WASH sector.
Amongst others, the Water Point Mapping -WPM- (WaterAid and ODI, 2005), the UNICEF-
supported Multiple Indicator Cluster Survey -MICS- (United Nations Children’s Fund, 2006), the
Rapid Assessment of Drinking Water Quality -RADWQ- (Howard et al., 2003, draft), and the
Water Safety Plans (Bartram et al., 2009). However, methodological problems arise when they are
implemented at local scale to produce reliable inputs for planning support.
First critical shortcoming is related to the type of data required to monitor the sector, since different
information sources may be required (Joint Monitoring Programme, 2012b). Household surveys are
by large the most commonly used tools for collecting WASH data (Joint Monitoring Programme,
2006; Macro International Inc, 1996; United Nations Children’s Fund, 2006). But a focus on
households is not sufficient to answer many relevant questions, and hence needs to be supplemented
with data from other sources. For instance, an audit at the water point might provide insight into
operational and management-related aspects of the service. A methodology to efficiently combine
these two types of information sources should have potential for wider implementation.
Another key limitation is that of comparability (Joint Monitoring Programme, 2006), since a variety
of indicators are being simultaneously employed to measure different aspects of the level of service.
More often than not, to assess trends over periods of time or to compare indicators regionally has
therefore remained challenging. As a first step against this comparability problem, the Joint
Monitoring Programme for Water Supply and Sanitation (JMP) formulated a set of harmonized
survey questions (Joint Monitoring Programme, 2006) to provide worldwide reliable estimates of
drinking-water and sanitation coverage at national level (Joint Monitoring Programme, 2012a). In
so doing, JMP has improved the processes and approaches to monitoring the sector, though the
definitions employed have been criticised as being too infrastructure-based. (Giné Garriga and
Pérez Foguet, 2012, Under review-b; Giné et al., 2011; Hunt, 2001; Jiménez and Pérez-Foguet,
2012). Today, an ongoing consultative process is debating a consolidated proposal of targets and
indicators for the post-2015 monitoring framework (Joint Monitoring Programme, 2011; Joint
Monitoring Programme, 2012c).
The techniques employed for data acquisition also play a key role in terms of data reliability and
validity (United Nations Children’s Fund, 2006). A well-designed questionnaire helps elicit a
response that is accurate and measures the things one seeks to measure. On the other hand,
interviews with predetermined and closed-end questions are not conducive to study respondent’s
perceptions or motivations (Grosh, 1997), thus pointing out the need for employing alternative
survey instruments to avoid bias in survey’s outcomes. For instance, water quality should be
bacteriologically tested (Howard et al., 2003, draft; Jiménez and Pérez-Foguet, 2012; Joint
Monitoring Programme, 2011); while study of handwashing through structured observation may
help avoid over-reporting of “desirable” hygiene behaviours (Manun'Ebo et al., 1997).
Finally, there is an issue with the statistical precision of the estimates. A common monitoring need
in local decision-making is to assess separately the performance of the lowest administrative
subunits (e.g. communities, villages, etc.) in the area of interest (e.g. district, municipality, etc.).
Since the number of these administrative subunits is generally large, the level in which information
needs to be disaggregated is high, and one is therefore faced with the need to balance precision
against cost when deciding the size of the sample (Bennett et al., 1991; Grosh, 1997; Lwanga and
Lemeshow, 1991). Moreover, a scientifically valid sampling methodology is necessary to achieve
reliable estimates. For household surveys, a cluster sampling design has proved a practical solution
(Bennett et al., 1991; Lemeshow and Stroh, 1988; United Nations Children’s Fund, 2006). And
water point mapping exercises, where a comprehensive record of water sources is undertaken (i.e.
no sampling), have also been successfully implemented to monitor the distribution and status of
water supplies (WaterAid, 2010).
In sum, a need for further research into feasible alternatives for data collection to the currently used
strategies has been highlighted (Joint Monitoring Programme, 2011), and the purpose of this study
is to present a new specific approach for the WASH sector at local level, as an attempt to overcome
previous shortcomings. It takes the WPM as a starting point to record all available water sources at
a particular location, which results in the need of covering the whole area of intervention. This
information is then combined with data provided from a household-based survey, in which a
representative sample of households is selected to assess sanitation and hygiene habits. In brief,
taking advantage of the current momentum of WPM as field data collection method in the water
sector (Government of Liberia, 2011; Government of Sierra Leone, 2012; Jiménez and Perez-
Foguet, 2011; Pearce and Howman, 2012; WaterAid, 2010) and the growing interest among
development stakeholders in harmonizing sector monitoring (Joint Monitoring Programme, 2012c),
this study suggests a cost-efficient alternative to simultaneously perform a WPM together with a
household survey, thus producing a comprehensive WASH database as a valuable output for
policymaking. To test the applicability and validity of the proposed approach, three different case
studies in East Africa are presented.
In Section 2, basic concepts of the evaluation framework employed in this study are outlined. The
methodology proposed to collect WASH primary data is described in Section 3. It presents the three
case studies and highlights key features of the approaches adopted in each one of them. Section 4
computes statistical validity of the method and, in so doing, provides useful guidelines on data
exploitation for decision-makers. Integral to this discussion there are a variety of alternatives to
disseminate achieved results, in an effort to provide clear and accurate policy messages. The paper
concludes that efficient data collection mechanisms can be designed to produce reliable estimates
for local planning processes. Their implementation in the real world, however, is to a certain extent
elusive; and specific challenges that remain unaddressed are pointed out as ways forward.
2. EVALUATION FRAMEWORK
This section introduces core aspects of the evaluation framework proposed to locally assess the
WASH status. First, the two methodologies for data collection in which we base our approach are
presented, i.e. the Water Point Mapping (WPM) and the Multiple Indicator Cluster Survey (MICS).
Second, it discusses the issue of the sample size, as the survey design has to enable the compilation
of accurate primary data to produce statistically representative estimates. Third, a reduced set of
measurable indicators is proposed as the basis of the monitoring strategy.
2.1. The Water Point Mapping
Mapping of water points has been in use by NGOs and agencies worldwide for over a decade,
particularly in sub-Saharan Africa (e.g. Malawi, Tanzania, Ghana, Ethiopia, Zambia, Liberia, Sierra
Leone, etc.). This methodology, largely promoted by the NGO WaterAid, can be defined as an
‘exercise whereby the geographical positions of all improved water points
in an area are gathered
in addition to management, technical and demographical information’ (WaterAid and ODI, 2005).
WPM involves the presentation of these data in a spatial context, which enables a rapid
visualization of the distribution and status of water supplies. A major advantage is that water point
maps provide a clear message on who is and is not served; and particularly in rural areas, they are
being used to highlight equity issues and schemes’ functionality levels at and below the district
level. This information can be employed to inform decentralized governments about the planning of
investments to increase water coverage (Jiménez and Pérez-Foguet, 2010; WaterAid, 2010).
Specifically, the mapping does not refer to a fixed set of indicators, and two different actions are
suggested in this regard: i) biological testing to ensure water quality; and ii) the inclusion of
unimproved sources. First, water quality analysis has long been nearly absent from water coverage
assessments because of affordability issues (Howard et al., 2003, draft; Joint Monitoring
Programme, 2010). In the absence of such information, it is assumed that certain types of water
The types of water points considered as improved are consistent with those accepted internationally by the
WHO/UNICEF Joint Monitoring Programme Joint Monitoring Programme. Core questions on drinking-water and
sanitation for household surveys. WHO / UNICEF, Geneva / New York, 2006. More specifically, an improved water
point is a place with some improved facilities where water is drawn for various uses such as drinking, washing and
cooking Stoupy O, Sugden S. Halving the Number of People without Access to Safe Water by 2015 – A Malawian
Perspective. Part 2: New indicators for the millennium development goal. WaterAid, London, 2003.
supplies categorized as ‘improved’ are likely to provide water of better quality than traditional
unimproved sources (Joint Monitoring Programme, 2000; Joint Monitoring Programme, 2012a).
This assumption, though, appears over-optimistic, and improved technologies do not always deliver
safe water (Giné Garriga and Pérez Foguet, 2012, Under review-b; Jiménez and Pérez-Foguet,
2012; Sutton, 2008). Contrary to what might be expected, and particularly in comparison with
overall investments projected for new infrastructure or with ad hoc quality testing campaigns, water
quality surveillance does not significantly impact on the overall cost of the mapping exercise: from
USD 12 to 15 dollars/waterpoint in standard WPM (Stoupy and Sugden, 2003) up to USD 20 when
quality testing is included (Jiménez and Pérez-Foguet, 2012). Second, being the original focus of
WPM on improved waterpoints, unimproved sources may be also mapped if they are accessed for
domestic purposes. A thorough analysis of collected data would shed light on the suitability of the
improved / unimproved classification proposed by the JMP, but more importantly, this would help
understand equity issues in service delivery (Giné Garriga and Pérez Foguet, 2012, Under review-b;
Jiménez and Pérez-Foguet, 2011; Joint Monitoring Programme, 2012a).
2.2. Household Survey
A major strength of WPM is, per definition, comprehensiveness with respect to the sample of water
points audited, which entails complete geographic representation of all strata in the study area (i.e.
all enumeration areas as communities, villages, etc.). Taking advantage of this logistic arrangement,
and in addition to the mapping, a household-based survey may be thus designed to evaluate
sanitation and hygienic practices at the dwelling. As it may be assumed that all households are
located within walking distance of one water source (either improved or unimproved), the approach
adopted practically ensures full inclusion of families in the sampling frame.
In terms of technique, the design and selection of the sample draws on the MICS, i.e a methodology
developed by UNICEF (United Nations Children’s Fund, 2006) to collect social data, which is
ultimately required amongst others for monitoring the goals and targets of the Millennium
Declaration or producing core United Nations’ development indices. The study population is
stratified into a number of small mutually exclusive and exhaustive groups, so that members of one
group cannot be simultaneously included in another group. In this study, however, main difference
is that when sampling, a sample of households is selected from each stratum (stratified sampling),
rather than selecting a reduced number of strata, from which a subsample of households is identified
(cluster sampling). In so doing, the risk of homogeneity within the strata remains relatively low,
thus reducing the need for applying any correction factor in sample size determination, i.e. the
”. A “design effect” of 1 is accepted in stratified random sampling, though ten-fold
or even higher variations are not uncommon values in cluster samplings with large cluster’s sizes
(Kish, 1980). In a WASH cluster survey, a value of 4 may be appropriate as acknowledged by the
United Nations Children’s Fund (2009).
2.3. Sample size and precision
In local decision-making, of interest is the evaluation of the level of service for the recipient
administrative unit as a whole. However, acknowledging that administrative subunits may have
uneven coverage, there is also concern for estimating their performance to identify the most
vulnerable areas. In other words, one regional coverage value might be sufficient from the
viewpoint of central governments; but since such value says nothing about local variations,
estimates at the lowest administrative scale are required for decentralised planning. To produce
local robust estimates substantially increases the required size of the sample, which directly affects
the cost of the survey.
The goal of WPM is to develop a comprehensive record of all water points available in the area of
intervention. There is thus no need of sampling. For the household survey, in contrast, a statistically
representative sample needs to be selected. The basic sampling unit is the household, and the size of
2 The “design effect” is an adjustment that measures the efficiency of the sample design, and is calculated by the ratio
of the variance of an estimator to the variance of the same estimator computed under the assumption of simple random
a representative sample n is numerically given by Cochran (1977, third edition):
is the confidence level, and z is a constant which relates to the normally distributed estimator of the
specified level. For a confidence level of 95% (
= 0.05), the value of
is 1.96 (
when α is 0.1;
= 1.28 when α is 0.2);
p is the assumed proportion of households giving a particular response for one given question. The
“safest” choice is a figure of 0.5, since the sample size required is largest when p = 0.5;
D = is the sample design effect. As mentioned, D = 1 in stratified random sampling. However,
acknowledging that a complete random exercise for household selection is almost unachievable in each
stratum, a value of 2 is recommended. It is noteworthy that in comparison with the sampling plan
required in a standard cluster survey, where D = 4 (United Nations Children’s Fund, 2009), the
sampling approach adopted in this study halves the sample size, which considerably reduces the overall
cost of the data collection exercise; and
d is the required precision on either side of the proportion. A typically used figure in similar surveys is d
= ± 0.05, based on the argument that lower precision would produce unreliable results while a higher
precision would be too expensive as it would require a very large survey. This precision may be
considered at highest scale of intervention. Estimates at lower administrative scale should be assessed
with lower precision; i.e. d = ± 0.10 or ± 0.15.
As an example, a minimum sample size n of 192 would be required to produce estimates in each
administrative subunit within 20% (± 10%) of the true proportion with 95% confidence (D = 2). If
the sample design effect is estimated as 4, twice the number of individuals would have to be studied
(i.e. 384) to obtain the same precision. From previous figures, however, it can be seen that Equation
1 is valid where populations are at least medium-size (N > 100). In contrast, when applied to more
reduced populations, it produces unachievable figures or large sampling errors. For use in local
household-based surveys, Giné Garriga & Pérez Foguet (2012, Under review-a) proposed an
alternative approach to determine the sample size, in which the practitioner may easily identify the
sampling plan that best balances precision and cost (Table 1).
2.4. Water, sanitation and hygiene indicators
A core element of any evaluation framework is the set of indicators in which base the analysis. It is
evident that from a WASH perspective, a wide range of variables exists to assess the current status
of service level. Of particular interest is the recently adopted human rights framework, which
reflects the concept of progressive realization in the level of service and requires the definition of
specific indicators to deal with the issues of affordability, quality, reliability and non-
discrimination, amongst others; or the debate guided by WHO and UNICEF about the post-2015
monitoring of WASH (Joint Monitoring Programme, 2011; Joint Monitoring Programme, 2012c).
To exactly identify what should be measured remains challenging, though, and rules of thumb for
the selection process include the following criteria. First, in terms of efficiency, the number of
indicators should be as reduced as possible but sufficient to ensure a thorough description of the
context in which the service is delivered (Joint Monitoring Programme, 2011; United Nations
Children’s Fund, 2006). Second, to resolve the comparability problems, survey questions need to be
harmonized with those internationally accepted (Joint Monitoring Programme, 2006; Joint
Monitoring Programme, 2012c). Finally, indicators should be EASSY (Jiménez et al., 2009): Easily
measurable at local level, Accurately defined, Standardized and compatible with data collected
elsewhere, Scalable at different administrative levels, and Yearly updatable. In Table 2, a short list
of core indicators is summarized.
Moreover and beyond average attainments, it is accepted that any evaluation framework should
identify the high-risk groups in which policy-makers may prioritize efforts and resources (Joint
Monitoring Programme, 2012c). The concern is to identify gaps in WASH outcomes between the
poor and the better off. To do this, one option is to employ “direct” measures of living standards,
such as household income or expenditure, though they are often unreliable (Filmer and Pritchett,
2001). Another approach is to use a “proxy” measure, such as a wealth index constructed from
information on household ownership of durable goods (Booysen et al., 2008; Filmer and Pritchett,
2001; O’Donnell et al., 2007), education level of household-head, sex of household head, etc. These
data promote evidence-based pro-poor planning and targeting processes, and ultimately help
improve service level of the most vulnerable.
As abovementioned, the approach adopted for data collection combines a mapping of water sources
with a stratified survey of households. Different methodologies exist which combine the waterpoint
and the household as key information sources, but they commonly differ from the method proposed
herein in i) the focus -national rather than local-, and in ii) the statistical precision of the estimates -
inadequate to support local level decision-making-. Key features of the proposed methodology
include i) an exhaustive identification of enumeration areas (administrative subunits as
communities, villages, etc.); ii) audit in each enumeration area of all improved and unimproved
water points accessed for domestic purposes; and iii) random selection of a sample size of
households that is representative at the local administrative level (e.g. district, municipality, etc.)
The mapping of waterpoints is exhaustive regardless functionality issues, though the inclusion of
unimproved sources in the analysis will be dependent on the scope of the exercise and available
resources. The need to tackle equity issues has been highlighted from the viewpoint of human
rights, and any exercise covering unimproved waterpoints would provide inputs to elucidate the
access pattern of the population. In rural contexts, however, this type of water source may be
common, thus increasing significantly the budget and resources devoted to data collection in case of
inclusion. Similarly, where main water technology is piped systems with household connections,
the idea of a comprehensive audit of all these private points-of-use is practically impossible. A more
convenient solution would be to visit the distribution tank and a reduced number of domestic taps,
which are taken as representative of the overall system.
The household survey is conducted in parallel with the mapping. Ideally, a defined number of
households will be selected in a statistically random manner from a comprehensive list of all
households in the subunit of study. However, such a list does often lack. Then, if the population
size is small, the optimum alternative may be to create a list by carrying out a quick census. In those
cases where enumerating all households is impracticable, literature suggests different sampling
techniques to achieve a random or near-random selection (Bennett et al., 1991; Frerichs and Tar,
1989; Lemeshow and Stroh, 1988). They usually involve two stages: the identification of one or
various households to be the starting point, and a method for selecting “n” successive households,
preferably spread widely over the community. In the end, where a complete random exercise is not
achievable, any methodology during the sampling process which promotes that the sample is as
representative as possible would be acceptable, as long as it is clear and unambiguous, and does not
give the enumerator the opportunity to make personal choices which may introduce bias. In these
cases, however, and to ensure data validity, to apply a correction factor in sample size determination
(D = 2) is recommended.
In terms of technique, the method relies on a variety of mechanisms to assure quality of produced
outcomes. Among the most important are:
- Territorial delimitation of study area. As an exercise to support planning, administrative
subunits in which base data collection should play a relevant administrative role in
decentralised service delivery. Thus, they should be adequately delimited, unambiguous and
well-known by both decision-makers and local population.
- Design of survey instruments. On the basis of a reduced set of reliable and objective
indicators (Table 2), appropriate survey tools should be developed for an accurate
assessment of the WASH status. This study is reliant on a combination of quantitative and
qualitative study tools, which are specially designed to collect data from the water point and
the household. Field inspections at the source employ a standardized checklist to evaluate
the existence, quality and functionality of the facility; and a water sample is also collected
for on-site bacteriological testing. At the dwelling, information related to service level is
captured through a structured interview administered to primary care-givers. In addition,
direct observation enables a complementary evaluation of domestic hygiene habits that may
not be otherwise assessed, as sanitary conditions of the latrine, existence and adequacy of
the handwashing facility, etc..
- Involvement and participation of local authorities. This study engages in various stages of
the process with those government bodies with competences in WASH. Specifically in data
collection, the commitment of officers belonging to the local government i) helps ensure a
link between field workers and the local structures at community level, and ii) promotes
sense of ownership over the process, as prerequisite for incorporating the data into decision-
making. As important of promoting collaborative data collection methods is to foresee the
viability of future data update activities, and accessibility and reliability of information have
been two core criteria when preparing the survey instruments. Moreover, a consultative
approach has been adopted for indicators’ definition to tailor the survey to each particular
context. Finally, data collection focuses on the administrative scale in which decisions are
based, thus producing relevant information for local policy-makers.
- Pilot study. A pilot run helps explore the suitability of the approach adopted, i.e.
methodology and study instruments. Further fine-tuning (question wording and ordering,
filtered questions, deletion of pointless questions, etc) follows the pilot.
- Data processing: The data entry process needs to be supervised, and the produced datasets
need to be validated on a regular basis. Various quality control procedures must be in place
to ensure that the data reflects the true position as accurately as possible, and routine
analysis of database or random checks of a reduced number of questionnaires may help
detect data inconsistencies and improve database robustness.
3.1. Study Area
Three different East African settings were selected as initial case studies to test the applicability and
validity of the proposed methodology, namely the district of Kibondo (Tanzania, in 2010), the
district of Homa Bay (Kenya, in 2011) and the municipality of Manhiça (Mozambique, in 2012).
The implementation of each case study adopted particular features, which are briefly summarized in
Table 3, and scope of work was designed on the basis of local needs (e.g. inclusion / exclusion of
unimproved waterpoints, visit to schools and health centres, the focus and level of detail required in
survey questionnaires, etc). However, they all shared same approach, method and goals: i) they
were formulated against specific call from a development-related institution to support local level
decision-making (in Tanzania, a Spanish NGO; in Kenya, UNICEF; and in Mozambique, the
Spanish Agency for International Development Cooperation); ii) the Research Group on
Cooperation and Human Development at the Technical University of Catalunya undertook overall
coordination of the study; iii) the local authority was engaged as principal stakeholder throughout
the process; and iv) a consultancy firm was contracted for field work support.
In previous section, a simplified approach to survey design for WASH primary data collection has
been outlined. The goal of the discussion first focuses on providing statistical robustness of the
methodology. To do this, we compute basic statistical parameters, in which also base the definition
of criteria that will help validate the collected data from the viewpoint of decision-making. Second,
and with the aim of communicating clear messages to policy-makers, a number of alternatives to
disseminate achieved results are presented.
4.1. Estimating the precision of a proportion
The data collected at the dwelling, because of the sampling strategy employed for households’
selection, require statistical validation. The ultimate goal is to guarantee reliability of any outcome
produced and thus avoid decisions based on false or misleading assumptions. To this end, estimates
of proportions may be calculated together with precision of those estimates, so that confidence
intervals can be assessed. As shown, all calculations described below are simple and can be easily
computed in any standard spread-sheet.
The proportion p, for example, of households in the i
subunit with access to improved sanitation is
given by Equation 2:
= number of households in the i
subunit with access to improved sanitation; and
= number of surveyed households in the i
When estimating the proportion at the overall administrative unit, population size of subunits should
be taken into consideration to avoid subunits’ under or overrepresentation. Therefore, achieved
responses should be weighted in proportion to the actual population of each subunit. To compute
the confidence limits for p
, we use the F distribution (Leemis and Trivedi, 1996), i.e. the so called
Clooper-Pearson interval (Reiczigel, 2003):
= 1 +
= 1 +
are the upper and lower limits of the confidence interval; and
is the confidence level. In this exercise, two different confidence levels have been employed for
calculation purposes, i.e. 90% (α = 0.1) and 80 % (α = 0.2).
Summary of aforementioned statistics (proportion and confidence interval) for the survey variables
(listed in Table 2) are presented in Tables 4 and 5 -Kenya-, 6 -Tanzania- and 7 -Mozambique-,
though on practical grounds, estimates of only few indicators are shown herein.
In decision-making and specifically to support targeting, one would opt to employ the proportion
and the confidence interval for a given variable to rank all the administrative subunits (Giné Garriga
and Pérez Foguet, 2012, Under review-a), where top positions would denote highest priority. From
Table 4, for instance, Rangwe could be easily identified as the most water poor division in Homa
= 0,355; p
= 0,753), in which thus focus policy attention.
Such prioritization, however, remains elusive where confidence intervals of the different subunits
overlap. As general rule, it can be seen (Tables 4 to 7) that lower levels of confidence (α increases)
give smaller confidence intervals, and hence reduced overlapping. Therefore, decision-makers
would need to balance precision of final estimates (α) against robustness of statistics for planning
purposes. For example, in Homa Bay and as regards time spent in water hauling (Table 4), one
could target differently Riana (pi = 0,910) and Nyarongi (pi = 0,936) for confidence level of 80%
(0,882 - 0,932 and 0,913 - 0,953 respectively), though such discrimination would not be feasible
with 90% confidence because of overlapping of the proportions with their corresponding intervals
(0,875 - 0,938 and 0,906 - 0,957 respectively).
One factor that challenges a reliable prioritization rank is the population variability in and within
different administrative subunits. Depending on the nature of the indicator, a homogeneous pattern
in the study area becomes more evident, primarily by i) reduced lengths of confidence intervals, and
ii) greater overlapping of interval estimates. More specifically, the data from the three case studies
show that indicators with low variability include gender disparities in the burden of collecting water
and point-of-use water treatment; while at the other end of the spectrum, indicators presenting
marked regional disparities are access to improved water supplies, and time spent in fetching water.
It might be concluded from the data that the local scale of analysis do not add value where domestic
habits and practices tend to a homogeneous behaviour, which would suggest that a regional estimate
then may be enough for planning purposes. However, and prior to validating previous assumption,
one would need to be sure that the indicator employed is adequate to describe patterns and trends in
a given context. For example, in a peri-urban setting as in Manhiça, it can be seen that the improved
/ unimproved classification of water supplies provides misleading information. Since the vast
majority of households access an improved source, the level of service is better described through
the availability of home connections (Table 7). As regards sanitation, it is gleaned from the
estimates of Table 5 and Table 6 that the approach adopted by the JMP does not lead to an obvious
discrimination among administrative subunits in rural areas as Homa Bay and Kibondo, while an
indicator related to open defecation status provides a clearer picture. In Manhiça, in contrast, the
opposite applies (Table 7). In sum, where the studied variable shows a strong homogeneous pattern,
the search for alternative indicators may be appropriate, since a larger sample size will probably
prove ineffective to distinguish between different population groups.
Despite the abovementioned restrictions, it is noteworthy that statistics shown in Tables 4 to 7
provide accurate inputs to policy-makers for the purpose of targeting. On the basis of performance
level and therefore taking the estimates of proportions as reference points, for a given indicator one
could group all subunits in different clusters, in such a way as to avoid overlap of their respective
confidence intervals. For instance in Kibondo (Table 5), four different prioritization groups may be
easily defined with regard to latrines’ sanitary conditions, which ultimately allows a transparent
identification of those subunits with poorest hygiene behaviour. Based on same approach and to
assess use of improved sanitation facility in Manhiça (Table 7), five target groups could be
As regards the information provided by the waterpoint mapping, the analysis may focus on
availability and geographic distribution of waterpoints. Without adequate combination of
demographic data, however, this information might be misleading. Hence access indicators are
usually assessed on the basis of standard assumption on the number of users per water source (i.e.
the source:man ratio, which in Kenya stands at 250 people per public tap). Two different
conclusions might be drawn from Table 8. First, it is observed that coverage levels of improved
water points at the household (see Table 4) or at the waterpoint are substantially different; i.e. the
standard source:man ratio is not followed up in practice. Second, access is dependent on the level of
service; e.g. one out of five families in Homa Bay (21,1%) may get drinking water from an
improved source, though this ratio is halved when water quality issues are taken into consideration.
4.2. Communicating clear messages to policymakers
The ultimate goal of sound sector-related data is to improve decision-making. To do this, two
elements are necessary (Grosh, 1997): the data must be analyzed to produce outcomes that are
relevant to the policy question, and the analysis must be disseminated and transmitted to
policymakers. Unless data is easily accessible and is presented in a user-friendly format, decision
makers will commonly do without the information. This section thus attempts to present a set of
survey outputs to demonstrate that the approach adopted in this study produces pertinent sector
data, which adequately exploited and disseminated might be employed by policy planners in
To begin, water and sanitation poverty maps are powerful instruments for displaying information
and enable non-technical audiences to easily understand the context and related trends (Henninger
and Snel, 2002). As observed from the tables discussed above, WASH-related poverty may follow a
highly heterogeneous pattern, widely varying between and within different administrative units; and
mapping permits a feasible visualization of such heterogeneity (Davis, 2002). In addition, it
provides a means for integrating data from different sources and from different disciplines
(Henninger and Snel, 2002), which helps provide a complete picture of the context in which the
service is delivered. In the end, mapping comes out an appropriate dissemination tool for sector
planning, monitoring and evaluation support.
The map in Figure 1, for example, shows the spatial distribution of improved water sources in
Homa Bay, and highlights the issues of functionality and seasonality. It is observed that the
majority of audited sources were found operational and with no seasonality problems (71%), despite
regional disparities. If such point-based information is combined with demographic data and the
source:man ratio cited above, a coverage density map can be developed (Figure 2) to show
accessibility rather than availability aspects. It may be gleaned from the map that, on average, only
8.7% of population are properly served by functional and year-round improved waterpoints, i.e. the
percentage of population covered if it is assumed that each community tap only serves 250 people.
Another concern in local decision-making is more related to the lack of transparent mechanisms to
establish needs and priorities. Ideally, the most vulnerable segments of the population should be
precisely targeted and then recipient of policy attention and public resources. And for this purpose,
rankings and league tables are powerful instruments. To denote priorities and specifically to define
prioritization criteria, two different approaches may be adopted. In terms of regional equity, the
goal would be to reach a minimum coverage threshold in every administrative subunit. But based on
an efficiency criterion, those subunits with highest number of potential beneficiaries should be first
targeted, regardless of coverage. A combination of both criteria would also be feasible, despite
resulting in a complex indicator.
As seen in Table 9, one different ranking is produced depending on each abovementioned criteria,
showing both ranks poor correlation. For example, it is observed that Riana is prioritized as its open
defecation-index stands at 67%, although in terms of potential beneficiaries, only roughly 60,000
people would beneficiate from the construction of new latrines. On the other hand, to defecate in the
open is less common in Asego (36%), while beneficiaries from a hypothetical intervention would be
raised up to 78.660. For planning purposes, the territorial equity criterion should be prioritized, as
vulnerability is probably higher where coverage is lower (Jiménez and Pérez-Foguet, 2010). After
targeting completion, each priority list could be easily related with specific remedial actions,
therefore translating development challenges into beneficial development activities.
Finally, few would dispute that pro-poor planning should be promoted to help address the issue of
non-discrimination. The underlying hypothesis is that service level is highly dependent on social
and economic conditions of population, which Table 10 confirms. From the data of two case studies
(Kenya and Mozambique), and despite of poor control of confounding effects on variables
measured, it is first observed that no significant differences exist with wealth regarding to access to
improved water sources (p
= 0.812 and p
= 0.14 respectively). A more in-depth analysis shows,
however, that piped water on premises is enjoyed mainly by the wealthiest (p
= < 0.001), while the
“poor” significantly spend more time spent in hauling water (p
= 0.016 and p
< 0.001). As
regards sanitation, it is noted that use of improved latrines is positively related to wealth (p
0.001 and p
= < 0.001). And for instance, the richest 25% of the population is almost ten (Homa
Bay) or six (Manhiça) times as likely to use an improved sanitation facility as the poorest quartile,
while the poorest 25% is two / twenty times more likely to practise open defecation than the richest
quartile. Much like water supply and sanitation, considerable differences are also found regarding to
hygiene practices between the rich and the poor. The percentage of households with adequate point-
of-use water treatment increases with wealth status (p
= 0.043 and p
= < 0.001). And socio-
economic status of the household shows strong association with safety in disposal of children’s
=0.028 and p
The delivery of water and sanitation services together with the promotion of hygiene is central to
public health. In recent years, service delivery has shifted to decentralised approaches; on the basis
that decentralisation will favour local needs and priorities. Any prospect to develop more pro-poor
policies, though, depends upon real efforts to strengthen the capacity of decentralised authorities.
Integral to this challenging process, and to enable policymakers to move from opaque to informed
discussions, accurate and reliable data at local level have to be accessible, i.e. routinely collected
and adequately disseminated. Against this background, the aim of this article is to develop a cost-
effective method for primary data collection which ultimately produces estimates accurate enough
to feed into decision-making processes.
First, a simplified survey design for WASH data collection is presented, which improves on other
existing methodologies in various ways. The approach adopted combines data from two different
information sources: the water point and the household. It takes the WPM as starting point, as a
method with increasing acceptance amongst governments and practitioners to inform the planning
of investments when improving water supply coverage. Since mapping entails as part of the survey
design specifications complete geographic representation of the study area, a stratified household-
based survey is undertaken in parallel, in which a sample of households is selected from each
stratum. In so doing, the risk of homogeneity within the strata remains relatively low, thus enabling
reduced “design effects” in sample size determination.
Second, the analysis of data in the discussion has produced valuable outputs that might be further
exploited for local level policymaking support. It has shown how data can feed into planning and
targeting decisions in a range of different ways. However, to offer relevant guidance to the policy
question, the analysis must be disseminated effectively. Maps or simple thematic indices are
adequate tools to capture the attention of policymakers, as they transmit a clear picture easily and
Nevertheless, it is noteworthy that specific challenges remain elusive to effectively improve
decentralised planning, primarily the continued use of the collected data in decision-making, and
the development of appropriate updating mechanisms (WaterAid, 2010). In the short term, the
effective exploitation of planning data by local decision-makers demands continued support from
multi-stakeholder alliances between governments, NGOs, academics and consultants. In the
medium term, however, political will and commitment at all levels, i.e. from central government to
local authorities, is imperative to ensure that improved use of data results in effective pro-poor
planning. Similarly, the evaluation framework needs to be rethought from the viewpoint of
sustainability, so that it could be updated autonomously by local stakeholders or replicated
elsewhere. In this regard, a major shortcoming is the trade-off between the scope and quality of the
data required for decision-making support and the complexity of updating mechanisms (WaterAid,
2010). These two challenges may suggest the way forward.
The authors would like to extend thanks to all families who participated in the study. Further thanks
go to ONGAWA and to Kibondo District Water Department for their support to undertake the
survey in Kibondo District, in Tanzania; to UNICEF (Kenya Country Office) and to Homa Bay
District Water Office and District Public Health Office, in Kenya; and to UN Habitat (Country
Office) and the Municipality of Manhiça, in Mozambique. This study has been partially funded by
the Centre de Cooperació per al Desenvolupament (Universitat Politècnica de Catalunya) and the
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Table 1 Sample size n for different values of N, α and d. Source: Giné Garriga & Pérez Foguet (2012a)
d < 0,1 d < 0,15 d < 0,20 d < 0,25 d < 0,1 d < 0,15 d < 0,20 d < 0,25 d < 0,1 d < 0,15 d < 0,20 d < 0,25
--- --- 7 6
--- --- 7 6
--- 7 6 5
--- 9 8 7
--- 9 8 7
--- 9 7 6
14 13 11 9
14 12 10 8
14 11 9 7
18 16 13 10
18 14 11 9
17 13 10 7
22 18 14 11
21 16 12 9
19 14 10
36 26 18 13
33 22 15 11
46 30 21 14
40 25 17
100 53 33 22 15 46 27 17 35
64 37 23
75 40 24
Eq. 1 96 43 24 15 67 30 17 11 41 18 10 7
Table 2 List of core WASH indicators
WASH Component Indicator Rationale
Access to improved water
% households with access to improved
Core water-related indicator. An improved source serves as a proxy
indicator for whether a household’s drinking-water is safe.
% of households adequately covered (based
on the standard source:man ratio)
To geographically show the least covered administrative subunits, i.e.
with less number of water points compared to the population living there.
Visit to all
One way distance to water
% of households spending, on average,
more than 30 minutes in fetching water
To assess whether the source is sufficiently close to the household to
ensure an adequate daily volume of water for basic domestic purposes. It
also help determine the saving in time of fetching water, as a major
expected benefit from the user’s side.
Individual collecting water
% households in which women shoulder the
burden in collecting water
This information helps identify gender and generational disparities with
respect to water-hauling responsibilities. It also ascertains who would
profit from bringing water closer to households.
Domestic water consumption
Average rate of per capita domestic water
consumption (based on of the number of
containers consumed per day and the rough
volume of these containers)
Distance to the water source may be an indirect indicator of water use,
but it is not accurate enough to draw conclusions. From the health
viewpoint, it is important to determine whether the volume of water
collected for basic needs reaches the minimum target value.
Operational status of water
% functional water points
To highlight sustainability issues, i.e. to identify operation and
Maintenance (O&M) problems and to assess the overall quality of the
Water quality (bacteriological
% bacteriological acceptable water sources
To evaluate water safety, specifically to determine presence of faecal
coliforms and few other critical parameters (pH, conductivity, turbidity
Seasonality of water
% year-round water sources
To identify seasonal or intermittent supplies, and to help assess reliability
of the service. A water point is considered to be seasonal if a seasonal
interruption in the supply of more than one month is reported. Where
seasonality is high, people often need to search for alternative sources
during dry season
% facilities with a functional and registered
A key sustainability aspect of the supply. For successful and sustainable
water schemes management, a proper institutional setting is required, and
at least functional water user committees need to be established.
% facilities with local access to technical
skills and spare parts
A key sustainability aspect of the supply. Access to skills and spares
promotes locally-based maintenance.
WASH Component Indicator Rationale
% of facilities in which at least 1 meeting
was held during last year to discuss income
and expenditure (both with the community
and the local authority)
A key sustainability aspect of the supply, particularly related to improve
transparency and accountability. Regular meetings are proxies of upward
and downward accountability, both on part of the local authority when
dealing with the water committee, and on the latter when tackling public
issues with beneficiaries
Pro-poor service delivery
% water entities which exempt vulnerable
houses from paying for water
A key community crosscutting issue. Fundamental human rights criteria
include accessibility, affordability and non-discrimination.
Access to and use of
% households with access to improved
Core water-related indicator. Important to check the current use of the
facility, rather than mere household’s ownership of the toilet.
% households practicing open defecation
To help distinguish between open defecation and latrine sharing, since both
practices are categorized as unimproved in JMP figures.
% households sharing improved sanitation
The shared status of a sanitation facility may entail poorer hygienic
conditions than facilities used by a single household.
% latrines maintained in adequate sanitary
Regardless the category of the toilet, it is important to determine whether
the maintenance of a facility undercut its hygienic quality and jeopardize a
continued use. Four proxies are verified: i) inside cleanliness, ii) presence
of insects, iii), smell and iv) privacy.
% latrines with appropriate handwashing
A rigorous assessment of handwashing behaviour would entail structured
observation -a prohibitively expensive exercise-. An assessment of the
adequacy of handwashing facilities, i.e. presence of soap and water, may be
an in-between solution.
Point-of-use water treatment
% households with adequate water
Disposal of children’s stools
% child caregivers correctly handling baby
Children’s faeces are the most likely cause of faecal contamination to the
immediate household environment.
Note: a) JMP indicator; b) Indicator proposed by JMP for Post-2015 Monitoring of Drinking-Water, Sanitation and Hygiene
Table 3 Key features of the approach adopted for data collection in each case study
Adm. Division Cost, in USD
Data collection Key features
Unit (Subunits) No. WPs
P (42%), T
(45%) and OC
986 IWPs 3.656 - The total area is 16,058 km
and the population is estimated at
414,764 (2002 Tanzania National Census).
- Sampling Plan (at ward level): α = 0.05; D = 2; d = ± 0.10; n
(min) = 192.
- Unimproved WPs were not audited. The WP audit included 38
questions (30 minutes per WP) + 1 water quality test..
- HH checklist included 18 questions related to sanitation and
domestic hygiene issues (10 minutes per HH).
- The field team included one staff from Spanish NGO, 1
technician from District Water Department, two staff from a
consultancy firm and two people from each visited village. Field
work was completed in 42 days.
P (74%), T
(17%) and OC
1.157 - The total area is 1,169.9 km
, and the total population is about
366,620 (2009 National Census).
- Sampling Plan (at division level): α = 0.05; D = 2; d = ± 0.10; n
(min) = 192.
- Unimproved WPs were audited in only 3 out of 5 divisions. The
WP audit included 38 questions (30 minutes per WP) + 1 water
- HH checklist included 65 questions related to water, sanitation
and domestic hygiene issues (35 minutes per HH).
- Data collection did not include urban areas. It included schools
(85) and health centres (37).
- The field team included tree staff from GRECDH - UPC (1 fully
involved), 1 technician from the District Water Department
(partially involved), 1 technician from the District Public Health
Department (partially involved), 8 staff from a consultancy firm,
and one people from each visited community. Field work was
completed in 33 days.
P (41%), T
(42%) and OC
1.229 - The total area is 250 km
and the population is estimated at
57,512 (2007 national estimates)
- Sampling Plan (at bairro level): α = 0.05; D = 2; d = ± 0.15; n
(min) = 86. Field work was completed in 39 days.
- Audit of improved and unimproved WPs. The WP audit included
41 questions (30 minutes per WP) + 1 water quality test
- HH checklist included 82 questions related to water, sanitation
and domestic hygiene issues (45 minutes per HH)
- Data collection included schools (16) and health centres (2)
- The field team included three staff from GRECDH - UPC (1 fully
involved), 3 technicians from the Vereação para Urbanização,
Construção, Água e Saneamento (partially involved), 14 staff
from a consultancy firm and 1 people from each visited village.
Field work was completed in 29 days.
Note: a) Includes data collection and data entry into the database. It does not include the cost of the portable kit for water quality analysis and
consumables. In percentage, overall budget broken down into personnel, transport, and others. b) Type of costs includes P for personnel; T for
Transport; and OC for Other costs. C) Type of waterpoints includes IWP for Improved waterpoint and UWP for unimproved waterpoint.
Table 4 Estimated proportion and Confidence Interval of water-related indicators in Homa Bay (Kenya)
Access to improved waterpoints
Time to fetch water
α = 0,1 α = 0,2
α = 0,1 α = 0,2
Asego 0,510 0,449 - 0,569 0,462 - 0,556
0,807 0,755 - 0,851 0,766 - 0,842
Rangwe 0,355 0,298 - 0,414 0,310 - 0,401
0,753 0,696 - 0,802 0,708 - 0,792
0,521 0,458 - 0,582 0,472 - 0,569
0,968 0,938 - 0,986 0,944 - 0,983
0,663 0,615 - 0,708 0,625 - 0,699
0,936 0,906 - 0,957 0,913 - 0,953
0,441 0,388 - 0,493 0,399 - 0,482 0,910 0,875 - 0,938 0,882 - 0,932
Note: a) Households spending less than 30 minutes for one round-trip to collect water. In colour (red
– orange – green), prioritization groups based on confidence intervals (α = 0,2)
Table 5 Estimated proportion and Confidence Interval of sanitation and hygiene indicators in Homa Bay (Kenya)
Use of improved facilities Open Defecation Disposal of children stools
Asego 0,172 0,129 - 0,220 0,137 - 0,210
0,358 0,302 - 0,416 0,313 - 0,404
0,903 0,837 - 0,948 0,851 - 0,940
Rangwe 0,125 0,088 - 0,170 0,095 - 0,160
0,430 0,370 - 0,490 0,383 - 0,477
0,864 0,774 - 0,926 0,793 - 0,916
0,125 0,087 - 0,171 0,094 - 0,161
0,531 0,469 - 0,592 0,482 - 0,579
0,714 0,622 - 0,794 0,641 - 0,778
0,140 0,108 - 0,177 0,114 - 0,169
0,630 0,581 - 0,676 0,592 - 0,666
0,437 0,361 - 0,513 0,377 - 0,497
0,073 0,048 - 0,104 0,052 - 0,097 0,667 0,615 - 0,714 0,626 - 0,704 0,752 0,682 - 0,812 0,697 - 0,800
Note: In colour (red – orange – green), prioritization groups based on confidence intervals (α = 0,2)
Table 6 Estimated proportion and Confidence Interval of WASH indicators in Kibondo District (Tanzania)
Use of Sanitation Latrine Conditions
α = 0,1
α = 0,2
α = 0,1
α = 0,2
Bunyambo 0,017 0,004 - 0,043 0,006 - 0,037
0,144 0,101 - 0,194 0,109 - 0,183
Busagara 0,049 0,025 - 0,083 0,029 - 0,075
0,259 0,206 - 0,317 0,217 - 0,305
0,021 0,007 - 0,047 0,009 - 0,041
0,086 0,054 - 0,127 0,060 - 0,117
0,072 0,043 - 0,112 0,048 - 0,103
0,133 0,093 - 0,182 0,101 - 0,171
0,036 0,016 - 0,066 0,019 - 0,059
0,087 0,056 - 0,127 0,062 - 0,118
0,043 0,021 - 0,076 0,025 - 0,069
0,081 0,050 - 0,122 0,056 - 0,113
0,017 0,004 - 0,043 0,006 - 0,037
0,074 0,044 - 0,115 0,049 - 0,106
0,077 0,042 - 0,126 0,048 - 0,116
0,134 0,087 - 0,193 0,095 - 0,180
0,038 0,017 - 0,069 0,021 - 0,062
0,314 0,257 - 0,374 0,268 - 0,361
0,029 0,011 - 0,059 0,013 - 0,052
0,149 0,105 - 0,201 0,113 - 0,190
0,013 0,002 - 0,039 0,003 - 0,033
0,057 0,029 - 0,096 0,034 - 0,087
0,017 0,004 - 0,042 0,006 - 0,036
0,139 0,098 - 0,188 0,106 - 0,177
0,055 0,030 - 0,088 0,035 - 0,081
0,229 0,180 - 0,282 0,190 - 0,271
0,000 0,000 - 0,016 0,000 - 0,012
0,098 0,063 - 0,143 0,070 - 0,133
0,000 0,000 - 0,014 0,000 - 0,010
0,086 0,056 - 0,124 0,061 - 0,115
0,035 0,015 - 0,068 0,018 - 0,061
0,097 0,061 - 0,143 0,068 - 0,133
0,028 0,011 - 0,057 0,013 - 0,050
0,094 0,061 - 0,138 0,067 - 0,128
0,009 0,001 - 0,029 0,002 - 0,024
0,047 0,025 - 0,077 0,029 - 0,071
0,006 0,000 - 0,026 0,000 - 0,021
0,067 0,038 - 0,105 0,043 - 0,097
0,029 0,012 - 0,055 0,015 - 0,049 0,206 0,160 - 0,257 0,169 - 0,246
Note: In colour (red – orange – green), prioritization groups based on confidence intervals (α = 0,2)
Table 7 Estimated proportion and Confidence Interval of WASH indicators in the Municipality of Manhiça
Access to water (piped on premises) Use of Sanitation Open Defecation
α = 0,1 α = 0,2
α = 0,1 α = 0,2
α = 0,1 α = 0,2
Manhiça Sede 0,907 0,831 - 0,955 0,848 - 0,947 0,587 0,485 - 0,682 0,506 - 0,663 0,013 0,000 - 0,061 0,001 - 0,050
Tsá-Tsé 0,218 0,143 - 0,308 0,157 - 0,289
0,218 0,143 - 0,308 0,157 - 0,289
0,038 0,010 - 0,096 0,014 - 0,083
0,440 0,342 - 0,541 0,361 - 0,520
0,373 0,279 - 0,474 0,298 - 0,453
0,067 0,026 - 0,135 0,032 - 0,120
0,321 0,233 - 0,418 0,250 - 0,397
0,372 0,280 - 0,470 0,298 - 0,450
0,000 0 - 0,037 0 - 0,029
0,064 0,025 - 0,130 0,031 - 0,115
0,103 0,052 - 0,177 0,060 - 0,161
0,051 0,017 - 0,113 0,022 - 0,099
0,000 0 - 0,041 0 - 0,032
0,229 0,148 - 0,326 0,163 - 0,305
0,229 0,148 - 0,326 0,163 - 0,305
0,000 0 - 0,039 0 - 0,030
0,080 0,035 - 0,151 0,042 - 0,136
0,067 0,026 - 0,135 0,032 - 0,120
0,000 0 - 0,039 0 - 0,030
0,067 0,026 - 0,135 0,032 - 0,120
0,347 0,255 - 0,447 0,273 - 0,426
0,000 0 - 0,039 0 - 0,030
0,013 0,000 - 0,061 0,001 - 0,050
0,613 0,511 - 0,707 0,532 - 0,688
0,286 0,202 - 0,382 0,218 - 0,361
0,208 0,134 - 0,298 0,148 - 0,279
0,026 0,004 - 0,079 0,006 - 0,067
0,533 0,432 - 0,632 0,452 - 0,612
0,187 0,116 - 0,276 0,129 - 0,257
0,013 0,000 - 0,061 0,001 - 0,050
0,987 0,938 - 0,999 0,949 - 0,998
0,880 0,799 - 0,935 0,817 - 0,926
0,000 0 - 0,039 0 - 0,030
0,573 0,471 - 0,670 0,492 - 0,650
0,333 0,243 - 0,433 0,261 - 0,412
0,000 0 - 0,039 0 - 0,030
0,038 0,010 - 0,096 0,014 - 0,083
0,115 0,061 - 0,192 0,070 - 0,176
0,244 0,165 - 0,336 0,180 - 0,316
0,960 0,899 - 0,989 0,913 - 0,985 0,440 0,342 - 0,541 0,361 - 0,520 0,013 0,000 - 0,061 0,001 - 0,050
Note: In colour (red – orange – green), prioritization groups based on confidence intervals (α = 0,2)
Table 8 Water-related estimates in Homa Bay (Kenya), from the WPM
Asego 94.950 33 31 No data 86.700 8,7% 8,2% 7,6% 5,8%
Rangwe 109.148 57 50 No data 94.898 13,1% 11,5% 9,8% 6,2%
Ndhiwa 59.211 24 21 19 53.211 10,1% 8,9% 6,3% 5,9%
Nyarongi 56.912 48 38 26 44.912 21,1% 16,7% 13,6% 11,0%
Riana 64.673 25 18 23 58.423 9,7% 7,0% 7,0% 4,3%
Note: IWPD: Improved waterpoint density; FIWPD: Functional improved waterpoint density; YRFIWPD: Tear-round functional
improved waterpoint density; BSFIWPD: Bacteriological safe functional improved waterpoint density.
Table 9 Priority ranks for access to sanitation, based on open defecation practice, in Homa Bay (Kenya)
α = 0.2
Riana 64.673 0,667 1 0,626 - 0,704 1 59.965 3
Rangwe 56.912 0,630 2 0,592 - 0,666 1 48.944 5
Ndhiwa 59.211 0,531 3 0,482 - 0,579 2 51.810 4
Nyarongi 109.148 0,430 4 0,383 - 0,477 3 95.505 1
Asego 94.950 0,358 5 0,313 - 0,404 4 78.660 2
Table 10 Access to water, sanitation and hygiene by wealth status
Pearson Chi-Square Exact Sig. (2-sided)
Homa Bay (Kenya)
Access to improved water sources p = 0.812 p = 0.140
One way distance to water source (km) p = 0.016 p < 0.001
Access to and use of improved sanitation p < 0.001 p < 0.001
Point-of-use water treatment p = 0.043 p < 0.001
Disposal of children’s stools p = 0.028 p = 0.007
Note: 1) In Pearson's chi-square test, the null hypothesis is independence, and the value p =
0.05 is used as the cut-off for rejection or acceptance
Figure 1 Distribution of functional improved water points, at location level
Figure 2 Density of year round and functional improved waterpoints, at location level