water–sanitation–hygiene mapping: an improved approach for data collection at local level

Science of the Total Environment 463–464 (2013) 700–711

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Science of the Total Environment

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Water–sanitation–hygiene mapping: An improved approach for datacollection at local level

Ricard Giné-Garriga a,⁎, Alejandro Jiménez-Fernández de Palencia a, Agustí Pérez-Foguet b

a Research Group on Cooperation and Human Development, University Research Institute for Sustainability Science and Technology, Universitat Politècnica de Catalunya,Campus Nord, Edifici VX, Pl Eusebi Güell, 6, Barcelona, Spainb Dept. of Applied Mathematics III, Research Group on Cooperation and Human Development, University Research Institute for Sustainability Science and Technology,Civil Engineering School, Universitat Politècnica de Catalunya, Campus Nord, Edifici C2, c/Jordi Girona 1–3, Barcelona, Spain

H I G H L I G H T S

• We present an integrated method for WASH-related data collection at local level.• The survey design combines a waterpoint mapping and a household survey.• Simple statistical analysis validates the data from the viewpoint of decision-making.• Data provide policymakers with evidences to inform planning and targeting processes.• We conclude that integrated data collection mechanisms can be designed to support local policymaking.

Abbreviations: JMP, Joint Monitoring Programme forMICS, Multiple Indicator Cluster Survey; NGO, NonUNICEF, United Nations Children's Fund; WASH, WaterWaterpoint; WPM, Water Point Mapping.⁎ Corresponding author at: Research Group on Coopera

University Research Institute for Sustainability SciencePolitècnica de Catalunya, Spain. Tel.: +34 405 43 75.

E-mail addresses: [email protected] (R. Giné-Garr(A.J.-F. de Palencia), [email protected] (A. Pérez-Fog

0048-9697/$ – see front matter © 2013 Elsevier B.V. Allhttp://dx.doi.org/10.1016/j.scitotenv.2013.06.005

a b s t r a c t
a r t i c l e i n f o
Article history:Received 2 December 2012Received in revised form 22 May 2013Accepted 2 June 2013Available online 10 July 2013

Editor: Simon James Pollard

Keywords:Data collectionData managementWater point mappingHousehold surveyWASHEast Africa

Strategic planning and appropriate development and management of water and sanitation services arestrongly supported by accurate and accessible data. If adequately exploited, these data might assist watermanagers with performance monitoring, benchmarking comparisons, policy progress evaluation, resourcesallocation, 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 ofcollected data, iii) inadequate combination of different information sources, and iv) statistical validity ofproduced estimates when disaggregated into small geographic subareas.This study proposes an improved approach for water, sanitation and hygiene (WASH) data collection atdecentralised level in low income settings, as an attempt to overcome previous shortcomings. The ultimateaim is to provide local policymakers with strong evidences to inform their planning decisions. The survey de-sign takes the Water Point Mapping (WPM) as a starting point to record all available water sources at a par-ticular location. This information is then linked to data produced by a household survey. Different surveyinstruments are implemented to collect reliable data by employing a variety of techniques, such as structuredquestionnaires, direct observation and water quality testing. The collected data is finally validated through simplestatistical analysis, which in turn produces valuable outputs that might feed into the decision-making process. Inorder 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–) arepresented.

© 2013 Elsevier B.V. All rights reserved.

Water Supply and Sanitation;-governmental organization;, Sanitation and Hygiene; WP,

tion and Human Development,and Technology, Universitat

iga), [email protected]).

rights reserved.

1. Introduction

Water and sanitation improvements together with good hygiene(WASH) produce evident effects on health population (Cairncross etal., 2010; Curtis and Cairncross, 2003; Esrey et al., 1991; Feachem,1984; Fewtrell et al., 2005). However, universal access to safe drink-ing water and basic sanitation remains a huge challenge in manylow income countries (Joint Monitoring Programme, 2012a), wherevast numbers of people are not properly provided for by these basicservices. To help end this appalling state of affairs, the sector has

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701R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711

been facing a gradual process of decentralisation, where the responsi-bility in service provision moves to local authorities. It is believed thatdecentralised governments have an informational advantage over thecentral government with regard to local needs and priorities, forwhich reason they are assumed to supply services in accordancewith demand, allocate resources more equitably, and ultimately con-ceive and implement policies with a focus on poverty reduction(Crook, 2003; Devas and Grant, 2003; Steiner, 2007). To effectivelydo this, local governments need to make evidence-based decisions,which primarily depend on the availability of accessible, accurateand reliable data that are routinely collected, disseminated andupdated. Amongst others, these data may be employed to i) measureprogress and performance, ii) improve transparency in budgetaryprocedures and promote increased investments in the sector, andiii) 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 policymakersis, at best, challenging (WaterAid, 2010). Limited capacities of recipientgovernmental bodies, inadequate sector-related institutional frame-work, and lack of data updating mechanisms are common reasonsthat hamper an adequate appropriation and continued use of the datafor planning and monitoring purposes (Joint Monitoring Programme,2011; World Health Organization, 2012).

In an effort to address one of the shortcomings cited above, i.e. thelack of reliable data, this study deals with the design of adequate meth-odologies for routine data collection. A variety of tools and techniqueshave been developed in recent years to collect primary data for theWASH sector. Amongst others, the Water Point Mapping –WPM–

(WaterAid and ODI, 2005), the UNICEF-supported Multiple IndicatorCluster Survey –MICS– (United Nations Children's Fund, 2006), theRapid Assessment of Drinking Water Quality –RADWQ– (Howard et al.,2003, draft), and theWater Safety Plans (Bartram et al., 2009). However,methodological problems arisewhen they are implemented at local scaleto produce reliable inputs for planning support.

First critical shortcoming is related to the type of data required tomonitor the sector, since different information sources may be required(Joint Monitoring Programme, 2012b). Household surveys are bylarge the most commonly used tools for collecting WASH data (JointMonitoring Programme, 2006; Macro International Inc., 1996; UnitedNations Children's Fund, 2006). But a focus on households is not sufficientto answermany relevant questions, and hence needs to be supplementedwith data from other sources. For instance, an audit at the water pointmight provide insight into operational and management-related aspectsof the service. Amethodology to efficiently combine these two types of in-formation sources should have potential for wider implementation.

Another key limitation is that of comparability (Joint MonitoringProgramme, 2006), since a variety of indicators are being simulta-neously employed to measure different aspects of the level of service.More often than not, to assess trends over periods of time or to com-pare indicators regionally has therefore remained challenging. As afirst step against this comparability problem, the Joint MonitoringProgramme for Water Supply and Sanitation (JMP) formulated a set ofharmonized survey questions (Joint Monitoring Programme, 2006) toprovide worldwide reliable estimates of drinking-water and sanitationcoverage at national level (Joint Monitoring Programme, 2012a). Inso doing, JMP has improved the processes and approaches to monitor-ing the sector, though the definitions employed have been criticisedas being too infrastructure-based. (Giné-Garriga and Pérez-Foguet,under review-b; Giné-Garriga et al., 2011; Hunt, 2001; Jiménez andPérez-Foguet, 2012). Today, an ongoing consultative process is debatinga consolidated proposal of targets and indicators for the post-2015monitoring framework (Joint Monitoring Programme, 2011; JointMonitoring Programme, 2012c).

The techniques employed for data acquisition also play a key role interms 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 theother hand, interviews with predetermined and closed-end ques-tions are not conducive to study respondent's perceptions or motiva-tions (Grosh, 1997), thus pointing out the need for employing alternativesurvey instruments to avoidbias in survey's outcomes. For instance,waterquality 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 helpavoid over-reporting of “desirable” hygiene behaviours (Manun'Ebo etal., 1997).

Finally, there is an issue with the statistical precision of the esti-mates. A common monitoring need in local decision-making is to as-sess 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 subunitsis generally large, the level in which information needs to bedisaggregated is high, and one is therefore faced with the need to bal-ance 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 necessaryto achieve reliable estimates. For household surveys, a cluster sam-pling 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 recordof water sources is undertaken (i.e. no sampling), have also been suc-cessfully implemented to monitor the distribution and status of watersupplies (WaterAid, 2010).

In sum, a need for further research into feasible alternatives fordata collection to the currently used strategies has been highlighted(Joint Monitoring Programme, 2011), and the purpose of this studyis to present a new specific approach for the WASH sector at locallevel, as an attempt to overcome previous shortcomings. It takes theWPM as a starting point to record all available water sources at a par-ticular location, which results in the need of covering the whole areaof intervention. This information is then combined with data provid-ed from a household-based survey, in which a representative sampleof households is selected to assess sanitation and hygiene habits. Inbrief, taking advantage of the current momentum of WPM as fielddata 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 growinginterest among development stakeholders in harmonizing sectormonitoring (Joint Monitoring Programme, 2012c), this study suggestsa cost-efficient alternative to simultaneously perform a WPM togeth-er with a household survey, thus producing a comprehensive WASHdatabase as a valuable output for policymaking. To test the applicabil-ity and validity of the proposed approach, three different case studiesin East Africa are presented.

In Section 2, basic concepts of the evaluation frameworkemployed in this study are outlined. The methodology proposed tocollect WASH primary data is described in Section 3. It presents thethree case studies and highlights key features of the approachesadopted in each one of them. Section 4 computes statistical validityof the method and, in so doing, provides useful guidelines on data ex-ploitation for decision-makers. Integral to this discussion there are avariety of alternatives to disseminate achieved results, in an effortto provide clear and accurate policy messages. The paper concludesthat efficient data collection mechanisms can be designed to producereliable estimates for local planning processes. Their implementationin the real world, however, is to a certain extent elusive; and specificchallenges that remain unaddressed are pointed out as ways forward.

2. Evaluation framework

This section introduces core aspects of the evaluation framework pro-posed to locally assess theWASH status. First, the twomethodologies for

702 R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711

data collection in which we base our approach are presented, i.e. theWater Point Mapping (WPM) and the Multiple Indicator Cluster Survey(MICS). Second, it discusses the issue of the sample size, as the survey de-sign has to enable the compilation of accurate primary data to producestatistically representative estimates. Third, a reduced set of measurableindicators is proposed as the basis of the monitoring strategy.

2.1. The Water Point Mapping

Mapping ofwater points has been in use byNGOs and agenciesworld-wide for over a decade, particularly in sub-Saharan Africa (e.g. Malawi,Tanzania, Ghana, Ethiopia, Zambia, Liberia, Sierra Leone, etc.). This meth-odology, largely promoted by the NGO WaterAid, can be defined as an‘exercise whereby the geographical positions of all improved waterpoints1 in an area are gathered in addition to management, technicaland demographical information’ (WaterAid and ODI, 2005). WPM in-volves the presentation of these data in a spatial context, which enablesa rapid visualization of the distribution and status of water supplies.A major advantage is that water point maps provide a clear message onwho is and is not served; and particularly in rural areas, they are beingused to highlight equity issues and schemes' functionality levels at andbelow the district level. This information can be employed to informdecentralized governments about the planning of investments to increasewater 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) biologicaltesting to ensure water quality; and ii) the inclusion of unimprovedsources. First, water quality analysis has long been nearly absentfrom water coverage assessments because of affordability issues(Howard et al., 2003, draft; Joint Monitoring Programme, 2010). Inthe absence of such information, it is assumed that certain types ofwater supplies categorized as ‘improved’ are likely to provide water ofbetter quality than traditional unimproved sources (Joint MonitoringProgramme, 2000; Joint Monitoring Programme, 2012a). This assump-tion, though, appears over-optimistic, and improved technologies donot always deliver safe water (Giné Garriga and Pérez-Foguet, underreview-b; Jiménez and Pérez-Foguet, 2012; Sutton, 2008). Contrary towhat might be expected, and particularly in comparison with overallinvestments projected for new infrastructure or with ad hoc qualitytesting campaigns, water quality surveillance does not significantlyimpact on the overall cost of the mapping exercise: from USD 12 to 15dollars/waterpoint in standard WPM (Stoupy and Sugden, 2003) up toUSD 20 when quality testing is included (Jiménez and Pérez-Foguet,2012). Second, being the original focus ofWPMon improvedwaterpoints,unimproved sourcesmay be alsomapped if they are accessed for domes-tic purposes. A thorough analysis of collected data would shed light onthe suitability of the improved/unimproved classification proposed bythe JMP, but more importantly, this would help understand equity issuesin service delivery (Giné-Garriga and Pérez-Foguet, under review-b;Jiménez and Pérez-Foguet, 2011; Joint Monitoring Programme, 2012a).

2.2. Household survey

A major strength of WPM is, per definition, comprehensivenesswith respect to the sample of water points audited, which entailscomplete geographic representation of all strata in the study area

1 The types of water points considered as improved are consistent with those ac-cepted internationally by the WHO/UNICEF Joint Monitoring Programme Joint Moni-toring Programme. Core questions on drinking-water and sanitation for householdsurveys. WHO/UNICEF, Geneva/New York, 2006. More specifically, an improved waterpoint is a place with some improved facilities where water is drawn for various usessuch as drinking, washing and cooking Stoupy O, Sugden S. Halving the Number of Peo-ple without Access to Safe Water by 2015 – A Malawian Perspective. Part 2: New indi-cators for the millennium development goal. WaterAid, London, 2003.

(i.e. all enumeration areas as communities, villages, etc.). Taking ad-vantage of this logistic arrangement, and in addition to the mapping,a household-based survey may be thus designed to evaluate sanita-tion and hygienic practices at the dwelling. As it may be assumedthat all households are located within walking distance of one watersource (either improved or unimproved), the approach adopted prac-tically ensures full inclusion of families in the sampling frame.

In terms of technique, the design and selection of the sample drawson the MICS, i.e. a methodology developed by UNICEF (United NationsChildren's Fund, 2006) to collect social data, which is ultimatelyrequired amongst others for monitoring the goals and targets of theMillennium Declaration or producing core United Nations' develop-ment indices. The study population is stratified into a number of smallmutually exclusive and exhaustive groups, so that members of onegroup cannot be simultaneously included in another group. In thisstudy, however, main difference is that when sampling, a sample ofhouseholds is selected from each stratum (stratified sampling), ratherthan selecting a reduced number of strata, from which a subsample ofhouseholds is identified (cluster sampling). In so doing, the risk ofhomogeneity within the strata remains relatively low, thus reducingthe need for applying any correction factor in sample size determina-tion, i.e. the “design effect2”. A “design effect” of 1 is accepted in strati-fied random sampling, though ten-fold or even higher variations arenot uncommon values in cluster samplings with large cluster's sizes(Kish, 1980). In aWASH cluster survey, a value of 4 may be appropriateas 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 levelof service for the recipient administrative unit as a whole. However,acknowledging that administrative subunits may have uneven cover-age, there is also concern for estimating their performance to identifythe most vulnerable areas. In other words, one regional coveragevalue might be sufficient from the viewpoint of central governments;but since such value says nothing about local variations, estimates atthe lowest administrative scale are required for decentralised plan-ning. To produce local robust estimates substantially increases the re-quired size of the sample, which directly affects the cost of the survey.

The goal of WPM is to develop a comprehensive record of all waterpoints available in the area of intervention. There is thus no need ofsampling. For the household survey, in contrast, a statistically repre-sentative sample needs to be selected. The basic sampling unit isthe household, and the size of a representative sample n is numerical-ly given by Cochran (1977, third edition):

n ¼ z21−α=2p 1−pð ÞDd2

ð1Þ

where:

α is the confidence level, and z is a constant which relates to thenormally distributed estimator of the specified level. For a con-fidence level of 95% (α = 0.05), the value of z1 − α/2 is 1.96(z1 − α/2 = 1.64whenα is 0.1; z1 − α/2 = 1.28whenα is 0.2);

p is the assumedproportion of households giving aparticular re-sponse for one given question. The “safest” choice is a figure of0.5, since the sample size required is largest when p = 0.5;

D is the sample design effect. As mentioned, D = 1 in stratifiedrandom sampling. However, acknowledging that a com-plete random exercise for household selection is almost

2 The “design effect” is an adjustment that measures the efficiency of the sample de-sign, and is calculated by the ratio of the variance of an estimator to the variance of thesame estimator computed under the assumption of simple random sampling.


unachievable in each stratum, a value of 2 is recommended. Itis noteworthy that in comparison with the sampling planrequired in a standard cluster survey, where D = 4 (UnitedNations Children's Fund, 2009), the sampling approachadopted in this study halves the sample size, which consider-ably reduces the overall cost of the data collection exercise;and

d is the required precision on either side of the proportion. Atypically used figure in similar surveys is d = ±0.05, basedon the argument that lower precision would produceunreliable results while a higher precision would be tooexpensive as it would require a very large survey. This pre-cision may be considered at highest scale of intervention.Estimates at lower administrative scale should be assessedwith lower precision; i.e. d = ±0.10 or ±0.15.

As an example, aminimumsample sizen of 192would be required toproduce estimates in each administrative subunit within 20% (±10%) ofthe true proportion with 95% confidence (D = 2). If the sample designeffect is estimated as 4, twice the number of individuals would have tobe studied (i.e. 384) to obtain the same precision. From previous figures,however, it can be seen that Eq. (1) is valid where populations are atleast medium-size (N > 100). In contrast, when applied to more re-duced populations, it produces unachievable figures or large samplingerrors. For use in local household-based surveys, Giné-Garriga andPérez-Foguet (under review-a) proposed an alternative approach todetermine the sample size, in which the practitioner may easily identifythe 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 inwhich base the analysis. It is evident that from a WASH perspective,a wide range of variables exists to assess the current status of servicelevel. Of particular interest is the recently adopted human rights frame-work, which reflects the concept of progressive realization in the levelof service and requires the definition of specific indicators to deal withthe issues of affordability, quality, reliability and non-discrimination,amongst others; or the debate guided by WHO and UNICEF about thepost-2015 monitoring of WASH (Joint Monitoring Programme, 2011,2012c). To exactly identifywhat shouldbemeasured remains challenging,though, and rules of thumb for the selection process include the followingcriteria. First, in terms of efficiency, the number of indicators should be asreduced as possible but sufficient to ensure a thorough description of thecontext in which the service is delivered (Joint Monitoring Programme,2011; United Nations Children's Fund, 2006). Second, to resolve thecomparability problems, survey questions need to be harmonizedwith those internationally accepted (Joint Monitoring Programme,

Table 1Sample size n for different values of N, α and d. Source: Giné-Garriga & Pérez-Foguet (und

N α = 95% α = 90%

d b 0.1 d b 0.15 d b 0.20 d b 0.25 d b 0.1 d b 0.15

8 – – 7 6 – –

10 – 9 8 7 – 915 14 13 11 9 14 1220 18 16 13 10 18 1425 22 18 14 11 21 1650 36 26 18 13 33 2275 46 30 21 14 40 25100 53 33 22 15 46 27150 64 37 23 53 29250 75 40 24 60500 87 43 67Eq. (1) 96 43 24 15 67 30

2006; Joint Monitoring Programme, 2012c). Finally, indicators shouldbe EASSY (Jiménez et al., 2009): Easily measurable at local level,Accurately defined, Standardized and compatible with data collect-ed elsewhere, Scalable at different administrative levels, and Yearlyupdatable. In Table 2, a short list of core indicators is summarized.

Moreover and beyond average attainments, it is accepted that anyevaluation framework should identify the high-risk groups in whichpolicy-makers may prioritize efforts and resources (Joint MonitoringProgramme, 2012c). The concern is to identify gaps in WASH out-comes between the poor and the better off. To do this, one option isto employ “direct” measures of living standards, such as householdincome or expenditure, though they are often unreliable (Filmerand Pritchett, 2001). Another approach is to use a “proxy” measure,such as awealth index constructed from information on household own-ership of durable goods (Booysen et al., 2008; Filmer and Pritchett, 2001;O'Donnell et al., 2007), education level of household-head, sex of house-hold head, etc. These data promote evidence-based pro-poor planningand targeting processes, and ultimately help improve service level ofthe most vulnerable.

3. Method

As abovementioned, the approach adopted for data collectioncombines a mapping of water sources with a stratified survey ofhouseholds. Different methodologies exist which combine thewaterpoint and the household as key information sources, but theycommonly differ from the method proposed herein in i) the focus –

national rather than local–, and in ii) the statistical precision of theestimates –inadequate to support local level decision-making–.Key features of the proposed methodology include i) an exhaustiveidentification of enumeration areas (administrative subunits as com-munities, villages, etc.); ii) audit in each enumeration area of all im-proved and unimproved water points accessed for domesticpurposes; and iii) random selection of a sample size of householdsthat is representative at the local administrative level (e.g. district,municipality, etc.) and below.

Themapping of waterpoints is exhaustive regardless functionality is-sues, though the inclusion of unimproved sources in the analysis will bedependent on the scope of the exercise and available resources. The needto tackle equity issues has been highlighted from the viewpoint ofhuman rights, and any exercise covering unimproved waterpointswould 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 datacollection in case of inclusion. Similarly, where main water technologyis piped systems with household connections, the idea of a comprehen-sive audit of all these private points-of-use is practically impossible.A more convenient solution would be to visit the distribution tank and

er review-a).

α = 80%

d b 0.20 d b 0.25 d b 0.1 d b 0.15 d b 0.20 d b 0.25

7 6 – 7 6 58 7 – 9 7 6

10 8 14 11 9 711 9 17 13 10 712 9 19 14 1015 11 28 1817 3217 35

39

17 11 41 18 10 7

Table 2List of core WASH indicators.

WASH component Indicator Rationale Source ofinformation

Technique

Watersupply

Access to improvedwater sourcesa, b

% households with access to improvedwater supply

Core water-related indicator. An improved source servesas a proxy indicator for whether a household'sdrinking-water is safe.

Household Direct questioning

% of households adequately covered(based on the standard source:manratio)

To geographically show the least covered administrativesubunits, i.e. with less number of water points comparedto the population living there.

Waterpoint Visit to allwaterpoints

One way distance towater source (km) a, b

% of households spending, on average,more than 30 min in fetching water

To assess whether the source is sufficiently close to thehousehold to ensure an adequate daily volume of waterfor basic domestic purposes. It also help determine thesaving in time of fetching water, as a major expectedbenefit from the user's side.


Individual collectingwatera

% households in which women shoulderthe burden in collecting water

This information helps identify gender and generationaldisparities with respect to water-hauling responsibilities.It also ascertains who would profit from bringing watercloser to households.


Domestic waterconsumption

Average rate of per capita domesticwater consumption (based on of thenumber of containers consumed per dayand the rough volume of thesecontainers)

Distance to the water source may be an indirect indicatorof water use, but it is not accurate enough to drawconclusions. From the health viewpoint, it is important todetermine whether the volume of water collected for basicneeds reaches the minimum target value.

Household Directquestioning/observation

Operational statusof water sourceb

% functional water points To highlight sustainability issues, i.e. to identify operationand Maintenance (O&M) problems and to assess theoverall quality of the O&M system.

Waterpoint Observation

Water quality(bacteriologicalcontamination)b

% bacteriological acceptable watersources

To evaluate water safety, specifically to determinepresence of faecal coliforms and few other criticalparameters (pH, conductivity, turbidity and nitrates)

Waterpoint Water qualitytesting(portable kit)

Seasonality of waterresourcesb

% year-round water sources To identify seasonal or intermittent supplies, and to helpassess reliability of the service. A water point is consideredto be seasonal if a seasonal interruption in the supply ofmore than one month is reported. Where seasonality ishigh, people often need to search for alternative sourcesduring dry season

Waterpoint Direct questioning

Managementsystemb

% facilities with a functional andregistered water committee

A key sustainability aspect of the supply. For successfuland sustainable water schemes management, a properinstitutional setting is required, and at least functionalwater user committees need to be established.


Maintenance system % facilities with local access to technicalskills and spare parts

A key sustainability aspect of the supply. Access to skillsand spares promotes locally-based maintenance.


Financial control % of facilities in which at least 1 meetingwas held during last year to discussincome and expenditure (both with thecommunity and the local authority)

A key sustainability aspect of the supply, particularlyrelated to improve transparency and accountability.Regular meetings are proxies of upward and downwardaccountability, both on part of the local authority whendealing with the water committee, and on the latter whentackling public issues with beneficiaries


Pro-poor servicedelivery

% water entities which exemptvulnerable houses from paying for water

A key community crosscutting issue. Fundamental humanrights criteria include accessibility, affordability andnon-discrimination.

Waterpoint/household

Direct questioning

Sanitation Access to and use ofsanitation

% households with access to improvedsanitationa, b

Core water-related indicator. Important to check the cur-rent use of the facility, rather than mere household'sownership of the toilet.

Household Observation

% households practicing open defecationb To help distinguish between open defecation and latrinesharing, since both practices are categorized asunimproved in JMP figures.


% households sharing improvedsanitation facilitiesa

The shared status of a sanitation facility may entail poorerhygienic conditions than facilities used by a single household.

Household Direct questioning/observation

Latrine conditions % latrines maintained in adequatesanitary conditions

Regardless the category of the toilet, it is important todetermine whether the maintenance of a facility undercutits hygienic quality and jeopardize a continued use. Fourproxies are verified: i) inside cleanliness, ii) presence ofinsects, iii), smell and iv) privacy.


Hygiene Handwashingdeviceb

% latrines with appropriate handwashingdevice

A rigorous assessment of handwashing behaviour wouldentail structured observation –a prohibitively expensiveexercise–. An assessment of the adequacy of handwashingfacilities, i.e. presence of soap and water, may be anin-between solution.


Point-of-use watertreatmenta

% households with adequate watertreatment

Household Direct questioning/observation

Disposal ofchildren's stoolsa, b

% child caregivers correctly handlingbaby excreta

Children's faeces are the most likely cause of faecalcontamination to the immediate household environment.


a JMP indicator.b Indicator proposed by JMP for Post-2015 monitoring of drinking-water, sanitation and hygiene.



a reduced number of domestic taps, which are taken as representative ofthe overall system.

The household survey is conducted in parallel with the mapping.Ideally, a defined number of households will be selected in a statisti-cally random manner from a comprehensive list of all householdsin the subunit of study. However, such a list does often lack. Then, ifthe population size is small, the optimum alternative may be to createa list by carrying out a quick census. In those cases where enumerat-ing all households is impracticable, literature suggests different sam-pling 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 orvarious households to be the starting point, and a method forselecting “n” successive households, preferably spread widely overthe community. In the end, where a complete random exercise is notachievable, any methodology during the sampling process which pro-motes that the sample is as representative as possible would be accept-able, as long as it is clear and unambiguous, and does not give theenumerator the opportunity to make personal choices which may intro-duce bias. In these cases, however, and to ensure data validity, to apply acorrection factor in sample size determination (D = 2) is recommended.

In terms of technique, the method relies on a variety of mecha-nisms to assure quality of produced outcomes. Among the most im-portant are:

– Territorial delimitation of study area. As an exercise to supportplanning, administrative subunits in which base data collectionshould play a relevant administrative role in decentralised servicedelivery. Thus, they should be adequately delimited, unambiguousand well-known by both decision-makers and local population.

– Design of survey instruments. On the basis of a reduced set of reli-able and objective indicators (Table 2), appropriate survey toolsshould be developed for an accurate assessment of the WASH sta-tus. This study is reliant on a combination of quantitative andqualitative study tools, which are specially designed to collectdata from the water point and the household. Field inspectionsat the source employ a standardized checklist to evaluate the exis-tence, quality and functionality of the facility; and a water sampleis also collected for on-site bacteriological testing. At the dwelling,information related to service level is captured through a structuredinterview administered to primary care-givers. In addition, directobservation enables a complementary evaluation of domestichygiene habits that may not be otherwise assessed, as sanitary con-ditions of the latrine, existence and adequacy of the handwashing fa-cility, etc..

– Involvement and participation of local authorities. This study engagesin various stages of the process with those government bodies withcompetences in WASH. Specifically in data collection, the commit-ment of officers belonging to the local government i) helps ensurea link between field workers and the local structures at communitylevel, and ii) promotes sense of ownership over the process, as pre-requisite for incorporating the data into decision-making. As impor-tant of promoting collaborative data collectionmethods is to foreseethe viability of future data update activities, and accessibility and re-liability of information have been two core criteria when preparingthe survey instruments. Moreover, a consultative approach hasbeen adopted for indicators' definition to tailor the survey to eachparticular context. Finally, data collection focuses on the administra-tive scale in which decisions are based, thus producing relevant in-formation for local policy-makers.

– Pilot study. A pilot run helps explore the suitability of the approachadopted, i.e. methodology and study instruments. Further fine-tuning (question wording and ordering, filtered questions, deletionof pointless questions, etc.) follows the pilot.

– Data processing: The data entry process needs to be supervised, andthe produced datasets need to be validated on a regular basis. Various

quality control procedures must be in place to ensure that the datareflects the true position as accurately as possible, and routine analy-sis of database or random checks of a reduced number of question-naires may help detect data inconsistencies and improve databaserobustness.

3.1. Study area

Three different East African settings were selected as initial case stud-ies to test the applicability and validity of the proposed methodology,namely the district of Kibondo (Tanzania, in 2010), the district of HomaBay (Kenya, in 2011) and the municipality of Manhiça (Mozambique, in2012). The implementation of each case study adopted particular fea-tures, which are briefly summarized in Table 3, and scope of work wasdesigned on the basis of local needs (e.g. inclusion/exclusion of unim-proved waterpoints, visit to schools and health centres, the focus andlevel of detail required in survey questionnaires, etc.). However, they allshared same approach, method and goals: i) they were formulatedagainst specific call from a development-related institution to supportlocal level decision-making (in Tanzania, a Spanish NGO; in Kenya,UNICEF; and in Mozambique, the Spanish Agency for InternationalDevelopment Cooperation); ii) the Research Group on Cooperation andHuman Development at the Technical University of Catalunya undertookoverall coordination of the study; iii) the local authority was engaged asprincipal stakeholder throughout the process; and iv) a consultancyfirm was contracted for field work support.

4. Discussion

In previous section, a simplified approach to survey design forWASH primary data collection has been outlined. The goal of thediscussion first focuses on providing statistical robustness of themethodology. To do this, we compute basic statistical parameters, inwhich also base the definition of criteria that will help validate thecollected data from the viewpoint of decision-making. Second, andwith the aim of communicating clear messages to policy-makers, anumber 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 strate-gy employed for households' selection, require statistical validation.The ultimate goal is to guarantee reliability of any outcome producedand thus avoid decisions based on false or misleading assumptions. Tothis end, estimates of proportions may be calculated together with pre-cision of those estimates, so that confidence intervals can be assessed.As shown, all calculations described below are simple and can be easilycomputed in any standard spread-sheet.

The proportion p, for example, of households in the ith subunitwith access to improved sanitation is given by Eq. (2):

pi ¼yini

ð2Þ

where:

yi number of households in the ith subunit with access toimproved sanitation; and

ni number of surveyed households in the ith subunit.

When estimating the proportion at the overall administrative unit,population size of subunits should be taken into consideration toavoid subunits' under or overrepresentation. Therefore, achieved re-sponses should be weighted in proportion to the actual population of

Table 3Key features of the approach adopted for data collection in each case study.

Case study Adm. division Cost, in USDa, b Data collection Key features

Unit (subunits) No. WPsc No. HH

Kibondo, Tanzania District(20 wards)

13.578 P (42%),T (45%) and OC (13%)

986 IWPs 3.656

− The total area is 16,058 km2 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 min per WP) + 1 water quality test.

− HH checklist included 18 questions related to sanitation and domestichygiene issues (10 min per HH).

− The field team included one staff from Spanish NGO, 1 technician fromDistrict Water Department, two staff from a consultancy firm and twopeople from each visited village. Field work was completed in 42 days.

Homa Bay, Kenya District(5 divisions)

32.389 P (74%), T (17%)and OC (9%)

255 187 IWPsand 68 UWPs

1.157 − The total area is 1,169.9 km2, 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 WPaudit included 38 questions (30 min per WP) + 1 water quality test.

− HH checklist included 65 questions related to water, sanitation and do-mestic hygiene issues (35 min per HH).

− Data collection did not include urban areas. It included schools (85) andhealth centres (37).

− The field team included tree staff from GRECDH–UPC (1 fully involved), 1technician from the District Water Department (partially involved), 1technician from the District Public Health Department (partially in-volved), 8 staff from a consultancy firm, and one people from each visitedcommunity. Field work was completed in 33 days.

Manhiça,Mozambique

Municipality(18 bairros)

23.719 P (41%), T (42%)and OC (17%)

228 224 IWPsand 4 UWPs

1.229 − The total area is 250 km2 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 ques-tions (30 min per WP) + 1 water quality test

− HH checklist included 82 questions related to water, sanitation anddomestic hygiene issues (45 min 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 eSaneamento (partially involved), 14 staff from a consultancy firm and 1people from each visited village. Field work was completed in 29 days.

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, overallbudget 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.


each subunit. To compute the confidence limits for pi, we use the F dis-tribution (Leemis and Trivedi, 1996), i.e. the so called Clooper–Pearsoninterval (Reiczigel, 2003):

pL ¼ 1þ n−yþ 1y F2y;2 n−yþ1ð Þ;1−α

2

!−1

ð3:1Þ

pU ¼ 1þ n−yyþ 1ð Þ F2 yþ1ð Þ;2 n−yð Þ;α2

!−1

ð3:2Þ

where:

pu and pl are the upper and lower limits of the confidence interval;and

α is the confidence level. In this exercise, two different confi-dence levels have been employed for calculation purposes,i.e. 90% (α = 0.1) and 80% (α = 0.2).

Summary of aforementioned statistics (proportion and confidenceinterval) for the survey variables (listed in Table 2) are presented inTables 4 and 5 –Kenya–, 6 –Tanzania– and 7 –Mozambique–, though

on practical grounds, estimates of only few indicators are shownherein.

In decision-making and specifically to support targeting, onewould opt to employ the proportion and the confidence interval fora given variable to rank all the administrative subunits (Giné-Garrigaand Pérez-Foguet, under review-a), where top positions woulddenote highest priority. From Table 4, for instance, Rangwe couldbe easily identified as the most water poor division in Homa Bay(pi, access = 0.355; pi, time = 0.753), in which thus focus policyattention.

Such prioritization, however, remains elusive where confidenceintervals of the different subunits overlap. As general rule, it canbe 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 finalestimates (α) against robustness of statistics for planning purposes.For example, in Homa Bay and as regards time spent in waterhauling (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 discriminationwould not be feasible with 90% confidence because of overlappingof the proportions with their corresponding intervals (0.875–0.938and 0.906–0.957 respectively).

Table 4Estimated proportion and confidence interval of water-related indicators in Homa Bay(Kenya).

Access to improved waterpoints Time to fetch watera

pi

pi

pL,i

- pU,i

pL,i

- pU,i

pL,i

- pU,i

pL,i

- pU,i

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

Ndhiwa 0,521 0,458 - 0,582 0,472 - 0,569 0,968 0,938 - 0,986 0,944 - 0,983

Nyarongi 0,663 0,615 - 0,708 0,625 - 0,699 0,936 0,906 - 0,957 0,913 - 0,953

Riana 0,441 0,388 - 0,493 0,399 - 0,482 0,910 0,875 - 0,938 0,882 - 0,932

α = 0,1 α = 0,2 α = 0,1 α = 0,2

Note: a) Households spending less than 30 min for one round-trip to collect water. In colour(red–orange–green), prioritization groups based on confidence intervals (α = 0.2).

Table 6Estimated proportion and confidence interval of WASH indicators in Kibondo District(Tanzania).

Ward

Use of Sanitation Latrine Conditions

pipipL,i -pU,i pL,i -pU,i pL,i - pU,i pL,i -pU,i

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

Gwunumpu 0,021 0,007 - 0,047 0,009 - 0,041 0,086 0,054 - 0,127 0,060 - 0,117

Itaba 0,072 0,043 - 0,112 0,048 - 0,103 0,133 0,093 - 0,182 0,101 - 0,171

Kakonko 0,036 0,016 - 0,066 0,019 - 0,059 0,087 0,056 - 0,127 0,062 - 0,118

Kasanda 0,043 0,021 - 0,076 0,025 - 0,069 0,081 0,050 - 0,122 0,056 - 0,113

Kasuga 0,017 0,004 - 0,043 0,006 - 0,037 0,074 0,044 - 0,115 0,049 - 0,106

Kibondo Mjini 0,077 0,042 - 0,126 0,048 - 0,116 0,134 0,087 - 0,193 0,095 - 0,180

Kitahana 0,038 0,017 - 0,069 0,021 - 0,062 0,314 0,257 - 0,374 0,268 - 0,361

Kizazi 0,029 0,011 - 0,059 0,013 - 0,052 0,149 0,105 - 0,201 0,113 - 0,190

Kumsenga 0,013 0,002 - 0,039 0,003 - 0,033 0,057 0,029 - 0,096 0,034 - 0,087

Mabamba 0,017 0,004 - 0,042 0,006 - 0,036 0,139 0,098 - 0,188 0,106 - 0,177

Misezero 0,055 0,030 - 0,088 0,035 - 0,081 0,229 0,180 - 0,282 0,190 - 0,271

Mugunzu 0,000 0,000 - 0,016 0,000 - 0,012 0,098 0,063 - 0,143 0,070 - 0,133

Muhange 0,000 0,000 - 0,014 0,000 - 0,010 0,086 0,056 - 0,124 0,061 - 0,115

Murungu 0,035 0,015 - 0,068 0,018 - 0,061 0,097 0,061 - 0,143 0,068 - 0,133

Nyabibuye 0,028 0,011 - 0,057 0,013 - 0,050 0,094 0,061 - 0,138 0,067 - 0,128

Nyamtukuza 0,009 0,001 - 0,029 0,002 - 0,024 0,047 0,025 - 0,077 0,029 - 0,071

Rugenge 0,006 0,000 - 0,026 0,000 - 0,021 0,067 0,038 - 0,105 0,043 - 0,097

Rugongwe 0,029 0,012 - 0,055 0,015 - 0,049 0,206 0,160 - 0,257 0,169 - 0,246

α = 0,1 α = 0,2 α = 0,1 α = 0,2

Note: In colour (red–orange–green), prioritization groups based on confidence intervals(α = 0.2).


One factor that challenges a reliable prioritization rank is the pop-ulation variability in and within different administrative subunits.Depending on the nature of the indicator, a homogeneous pattern inthe study area becomes more evident, primarily by i) reduced lengthsof confidence intervals, and ii) greater overlapping of interval esti-mates. More specifically, the data from the three case studies showthat indicators with low variability include gender disparities in theburden of collecting water and point-of-use water treatment; whileat the other end of the spectrum, indicators presenting marked re-gional disparities are access to improved water supplies, and timespent in fetching water. It might be concluded from the data thatthe local scale of analysis do not add value where domestic habitsand practices tend to a homogeneous behaviour, which wouldsuggest that a regional estimate then may be enough for planningpurposes. However, and prior to validating previous assumption,one would need to be sure that the indicator employed is adequateto describe patterns and trends in a given context. For example, in aperi-urban setting as in Manhiça, it can be seen that the improved/unimproved classification of water supplies provides misleading in-formation. Since the vast majority of households access an improvedsource, the level of service is better described through the availabilityof home connections (Table 7). As regards sanitation, it is gleanedfrom the estimates of Tables 5 and 6 that the approach adopted bythe JMP does not lead to an obvious discrimination among adminis-trative subunits in rural areas as Homa Bay and Kibondo, while anindicator related to open defecation status provides a clearer picture.In Manhiça, in contrast, the opposite applies (Table 7). In sum, wherethe studied variable shows a strong homogeneous pattern, the searchfor alternative indicators may be appropriate, since a larger samplesize will probably prove ineffective to distinguish between differentpopulation groups.

Despite the abovementioned restrictions, it is noteworthy that sta-tistics shown in Tables 4 to 7 provide accurate inputs to policy-makersfor the purpose of targeting. On the basis of performance level andtherefore taking the estimates of proportions as reference points, for agiven indicator one could group all subunits in different clusters, insuch a way as to avoid overlap of their respective confidence intervals.

Table 5Estimated proportion and confidence interval of sanitation and hygiene indicators in Homa

DivisionUse of improved facilities Ope

pipi

Asego 0,172 0,129 - 0,220 0,137 - 0,210 0,358 0,302 -

Rangwe 0,125 0,088 - 0,170 0,095 - 0,160 0,430 0,370 -

Ndhiwa 0,125 0,087 - 0,171 0,094 - 0,161 0,531 0,469 -

Nyarongi 0,140 0,108 - 0,177 0,114 - 0,169 0,630 0,581 -

Riana 0,073 0,048 - 0,104 0,052 - 0,097 0,667 0,615 -

pL,i -

α = 0

pL,i - pU,i pL,i - pU,i

α = 0,2α = 0,1

Note: In colour (red–orange–green), prioritization groups based on confidence intervals (α

For instance in Kibondo (Table 5), four different prioritization groupsmay be easily defined with regard to latrines' sanitary conditions,which ultimately allows a transparent identification of those subunitswith poorest hygiene behaviour. Based on the same approach and to as-sess use of improved sanitation facility in Manhiça (Table 7), five targetgroups could be established.

As regards the information provided by thewaterpointmapping, theanalysis may focus on availability and geographic distribution ofwaterpoints. Without adequate combination of demographic data,however, this informationmight bemisleading. Hence access indicatorsare usually assessed on the basis of standard assumption on the numberof users per water source (i.e. the source:man ratio, which in Kenyastands at 250 people per public tap). Two different conclusions mightbe drawn from Table 8. First, it is observed that coverage levels of im-provedwater points at the household (see Table 4) or at the waterpointare substantially different; i.e. the standard source:man ratio is notfollowed up in practice. Second, access is dependent on the level ofservice; e.g. one out of five families in Homa Bay (21,1%) may getdrinking water from an improved source, though this ratio is halvedwhen water quality issues are taken into consideration.

Bay (Kenya).

n Defecation Disposal of children stools

pipL,i - pU,i pL,i - pU,i

0,416 0,313 - 0,404 0,903 0,837 - 0,948 0,851 - 0,940

0,490 0,383 - 0,477 0,864 0,774 - 0,926 0,793 - 0,916

0,592 0,482 - 0,579 0,714 0,622 - 0,794 0,641 - 0,778

0,676 0,592 - 0,666 0,437 0,361 - 0,513 0,377 - 0,497

0,714 0,626 - 0,704 0,752 0,682 - 0,812 0,697 - 0,800

α = 0,2α = 0,1

pU,i pL,i - pU,i

α = 0,2,1

= 0.2).

Table 7Estimated proportion and confidence interval of WASH indicators in the Municipality of Manhiça (Mozambique).

Access to water (piped on premises) Use of Sanitation Open Defecation

Bairro pi pi pi

α = 0,1 α = 0,2

pL,i - pU,i pL,i - pU,i pL,i - pU,i pL,i -pU,i pL,i -pU,i pL,i -pU,i

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

Mulembja 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

Ribangue 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

Balocuene 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

Timaquene 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

Chibucutso 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

Mitilene 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

Ribjene 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

Cambeve 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

Maciana 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

Maragra 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

Matadouro 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

Chibututuine 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

Wenela 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)

α = 0,2α = 0,1 α = 0,2α = 0,1


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 datamust be analyzed to produce outcomes that are relevant to the policyquestion, and the analysis must be disseminated and transmitted topolicymakers. Unless data is easily accessible and is presented in auser-friendly format, decisionmakers will commonly do without the in-formation. This section thus attempts to present a set of survey outputsto demonstrate that the approach adopted in this study produces perti-nent sector data, which adequately exploited and disseminatedmight beemployed by policy planners in decision-making processes.

To begin, water and sanitation poverty maps are powerful instru-ments for displaying information and enable non-technical audiences toeasily understand the context and related trends (Henninger and Snel,2002). As observed from the tables discussed above, WASH-related pov-erty may follow a highly heterogeneous pattern, widely varying between

Table 8Water-related estimates in Homa Bay (Kenya), from the WPM.

Division Pop (2011) No. imp WPs Funct WPs No. unim WPs Un

Asego 94.950 33 31 No data 86Rangwe 109.148 57 50 No data 94Ndhiwa 59.211 24 21 19 53Nyarongi 56.912 48 38 26 44Riana 64.673 25 18 23 58

a IWPD: Improved waterpoint density;b FIWPD: Functional improved waterpoint density;c YRFIWPD: Year-round functional improved waterpoint density;d BSFIWPD: Bacteriological safe functional improved waterpoint density.

andwithin different administrative units; andmapping permits a feasiblevisualization of suchheterogeneity (Davis, 2002). In addition, it provides ameans for integrating data from different sources and from different dis-ciplines (Henninger and Snel, 2002), which helps provide a complete pic-ture of the context in which the service is delivered. In the end, mappingcomes out an appropriate dissemination tool for sector planning, moni-toring and evaluation support.

The map in Fig. 1, for example, shows the spatial distribution of im-proved water sources in Homa Bay, and highlights the issues of func-tionality and seasonality. It is observed that the majority of auditedsources were found operational and with no seasonality problems(71%), despite regional disparities. If such point-based information iscombined with demographic data and the source:man ratio citedabove, a coverage densitymap can be developed (Fig. 2) to show acces-sibility rather than availability aspects. It may be gleaned from the mapthat, on average, only 8.7% of population are properly served by func-tional and year-round improved waterpoints, i.e. the percentage of

served pop (by IWP) IWPDa FIWPDb YR — FIWPDc BS — FIWPDd

.700 8.7% 8.2% 7.6% 5.8%

.898 13.1% 11.5% 9.8% 6.2%

.211 10.1% 8.9% 6.3% 5.9%

.912 21.1% 16.7% 13.6% 11.0%

.423 9.7% 7.0% 7.0% 4.3%

Functional and year round

Functional but seasonal

Non-functional

Fig. 1. Distribution of functional improved water points, at location level.


population covered if it is assumed that each community tap only serves250 people.

Another concern in local decision-making is more related to thelack of transparent mechanisms to establish needs and priorities.Ideally, the most vulnerable segments of the population should beprecisely targeted and then recipient of policy attention and publicresources. And for this purpose, rankings and league tables are pow-erful instruments. To denote priorities and specifically to define prior-itization criteria, two different approaches may be adopted. In termsof regional equity, the goal would be to reach a minimum coveragethreshold in every administrative subunit. But based on an efficiencycriterion, those subunits with highest number of potential beneficia-ries should be first targeted, regardless of coverage. A combinationof both criteria would also be feasible, despite resulting in a complexindicator.

< 5%

5 - 10%

10 - 15%

15 - 25%

Equal or more than 25%

No Funct Imp WP

Fig. 2. Density of year round and functional improved waterpoints, at location level.

As seen in Table 9, one different ranking is produced depending oneach abovementioned criteria, showing both ranks poor correlation. Forexample, it is observed that Riana is prioritized as its open defecation-index stands at 67%, although in terms of potential beneficiaries, onlyroughly 60,000 people would beneficiate from the construction ofnew latrines. On the other hand, to defecate in the open is less commonin Asego (36%), while beneficiaries from a hypothetical interventionwould be raised up to 78.660. For planning purposes, the territorialequity criterion should be prioritized, as vulnerability is probably higherwhere coverage is lower (Jiménez and Pérez-Foguet, 2010). Aftertargeting completion, each priority list could be easily related with spe-cific remedial actions, therefore translating development challengesinto beneficial development activities.

Finally, fewwould dispute that pro-poor planning should be promot-ed to help address the issue of non-discrimination. The underlyinghypothesis is that service level is highly dependent on social and eco-nomic conditions of population, which Table 10 confirms. From thedata of two case studies (Kenya and Mozambique), and despite of poorcontrol of confounding effects on variables measured, it is first observedthat no significant differences exist with wealth regarding to access toimproved water sources (pHB = 0.812 and pM = 0.14 respectively). Amore in-depth analysis shows, however, that piped water on premisesis enjoyed mainly by the wealthiest (pM = b0.001), while the “poor”significantly spend more time spent in hauling water (pHB = 0.016and pM b 0.001). As regards sanitation, it is noted that use of improvedlatrines is positively related to wealth (pHB b 0.001 and pM = b0.001).And for instance, the richest 25% of the population is almost ten(Homa Bay) or six (Manhiça) times as likely to use an improved san-itation facility as the poorest quartile, while the poorest 25% is two/twenty times more likely to practise open defecation than therichest quartile. Much like water supply and sanitation, considerabledifferences are also found regarding to hygiene practices betweenthe rich and the poor. The percentage of households with adequatepoint-of-use water treatment increases with wealth status (pHB =0.043 and pM = b0.001). And socio-economic status of the householdshows strong association with safety in disposal of children's faeces(pHB = 0.028 and pM = 0.007).

5. Conclusions

The delivery of water and sanitation services together with thepromotion of hygiene is central to public health. In recent years, ser-vice delivery has shifted to decentralised approaches; on the basisthat decentralisation will favour local needs and priorities. Any pros-pect to develop more pro-poor policies, though, depends upon realefforts to strengthen the capacity of decentralised authorities. Integralto this challenging process, and to enable policymakers to move fromopaque to informed discussions, accurate and reliable data at locallevel have to be accessible, i.e. routinely collected and adequatelydisseminated. Against this background, the aim of this article is to devel-op a cost-effective method for primary data collection which ultimatelyproduces estimates accurate enough to feed into decision-makingprocesses.

First, a simplified survey design for WASH data collection ispresented, which improves on other existing methodologies in vari-ous ways. The approach adopted combines data from two differentinformation sources: the water point and the household. It takes theWPM as starting point, as a method with increasing acceptanceamongst governments and practitioners to inform the planning ofinvestments when improving water supply coverage. Since mappingentails as part of the survey design specifications complete geographicrepresentation of the study area, a stratified household-based surveyis undertaken in parallel, in which a sample of households is selectedfrom each stratum. In so doing, the risk of homogeneitywithin the stra-ta remains relatively low, thus enabling reduced “design effects” in sam-ple size determination.

Table 9Priority ranks for access to sanitation, based on open defecation practice, in Homa Bay (Kenya).

Division Population 2011 pi Rank A (equity) pL,i–pU,i

α = 0.2Rank A' (equity) No served population Rank B (efficiency)

Riana 64.673 0.667 1 0.626–0.704 1 59.965 3Rangwe 56.912 0.630 2 0.592–0.666 1 48.944 5Ndhiwa 59.211 0.531 3 0.482–0.579 2 51.810 4Nyarongi 109.148 0.430 4 0.383–0.477 3 95.505 1Asego 94.950 0.358 5 0.313–0.404 4 78.660 2


Second, the analysis of data in the discussion has produced valuableoutputs that might be further exploited for local level policymakingsupport. It has shown how data can feed into planning and targetingdecisions in a range of different ways. However, to offer relevantguidance to the policy question, the analysis must be disseminatedeffectively. Maps or simple thematic indices are adequate tools to cap-ture the attention of policymakers, as they transmit a clear picture easilyand accurately.

Nevertheless, it is noteworthy that specific challenges remain elusiveto effectively improve decentralised planning, primarily the continueduse of the collected data in decision-making, and the development of ap-propriate updatingmechanisms (WaterAid, 2010). In the short term, theeffective exploitation of planning data by local decision-makers demandscontinued support from multi-stakeholder alliances between govern-ments, NGOs, academics and consultants. In themedium term, however,political will and commitment at all levels, i.e. from central governmentto local authorities, is imperative to ensure that improved use of data re-sults in effective pro-poor planning. Similarly, the evaluation frameworkneeds to be rethought from the viewpoint of sustainability, so that itcould be updated autonomously by local stakeholders or replicated else-where. In this regard, a major shortcoming is the trade-off between thescope and quality of the data required for decision-making support andthe complexity of updating mechanisms (WaterAid, 2010). These twochallenges may suggest the way forward.

Acknowledgements

The authors would like to extend thanks to all families who partici-pated in the study. Further thanks go to ONGAWA and to KibondoDistrict Water Department for their support to undertake the surveyin 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 ofManhiça, in Mozambique. This study has been partially funded by theCentre de Cooperació per al Desenvolupament (Universitat Politècnicade Catalunya) and the Agencia Española de Cooperación Internacionalpara el Desarrollo [reference 11-CAP2-1562]. Financial support of theAgenciaGeneral d´Ajuts Universitaris i de Recerca -AGAUR- (Generalitat

Table 10Access to water, sanitation and hygiene by wealth status.

Indicator Pearson Chi-square exact sig.(2-sided)a

Homa Bay(Kenya)

Manhiça(Mozambique)

Access to improved water sources p = 0.812 p = 0.140One way distance to water source (km) p = 0.016 p b 0.001Access to and use of improved sanitation p b 0.001 p b 0.001Point-of-use water treatment p = 0.043 p b 0.001Disposal of children's stools p = 0.028 p = 0.007

a In Pearson's chi-square test, the null hypothesis is independence, and the valuep = 0.05 is used as the cut-off for rejection or acceptance.

de Catalunya) through the Beatriu de Pinos grant (BP-DGR 2011) toDr A. Jiménez is also acknowledged.

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