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 data collection 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, Spain b 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

HIGHLIGHTS

• 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. article info abstract

Article history: Strategic planning and appropriate development and management of water and sanitation services are Received 2 December 2012 strongly supported by accurate and accessible data. If adequately exploited, these data might assist water Received in revised form 22 May 2013 managers with performance monitoring, benchmarking comparisons, policy progress evaluation, resources Accepted 2 June 2013 allocation, and decision making. A variety of tools and techniques are in place to collect such information. Available online 10 July 2013 However, some methodological weaknesses arise when developing an instrument for routine data collection, Editor: Simon James Pollard 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 Keywords: produced estimates when disaggregated into small geographic subareas. Data collection This study proposes an improved approach for water, sanitation and hygiene (WASH) data collection at Data management decentralised level in low income settings, as an attempt to overcome previous shortcomings. The ultimate Water point mapping aim is to provide local policymakers with strong evidences to inform their planning decisions. The survey de- Household survey sign takes the Water Point Mapping (WPM) as a starting point to record all available water sources at a par- WASH ticular location. This information is then linked to data produced by a household survey. Different survey East Africa 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–;KibondoDistrict–Tanzania–; and Municipality of Manhiça –Mozambique–)are presented. © 2013 Elsevier B.V. All rights reserved.

1. Introduction

Water and sanitation improvements together with good hygiene Abbreviations: JMP, Joint Monitoring Programme for Water Supply and Sanitation; MICS, Multiple Indicator Cluster Survey; NGO, Non-governmental organization; (WASH) produce evident effects on health population (Cairncross et UNICEF, United Nations Children's Fund; WASH, Water, Sanitation and Hygiene; WP, al., 2010; Curtis and Cairncross, 2003; Esrey et al., 1991; Feachem, Waterpoint; WPM, Water Point Mapping. 1984; Fewtrell et al., 2005). However, universal access to safe drink- ⁎ Corresponding author at: Research Group on Cooperation and Human Development, ing water and basic sanitation remains a huge challenge in many University Research Institute for Sustainability Science and Technology, Universitat low income countries (Joint Monitoring Programme, 2012a), where Politècnica de Catalunya, Spain. Tel.: +34 405 43 75. E-mail addresses: [email protected] (R. Giné-Garriga), [email protected] vast numbers of people are not properly provided for by these basic (A.J.-F. de Palencia), [email protected] (A. Pérez-Foguet). services. To help end this appalling state of affairs, the sector has

0048-9697/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.scitotenv.2013.06.005 R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711 701 been facing a gradual process of decentralisation, where the responsi- accurate and measures the things one seeks to measure. On the bility in service provision moves to local authorities. It is believed that other hand, interviews with predetermined and closed-end ques- decentralised governments have an informational advantage over the tions are not conducive to study respondent's perceptions or motiva- central government with regard to local needs and priorities, for tions (Grosh, 1997), thus pointing out the need for employing alternative which reason they are assumed to supply services in accordance survey instruments to avoid bias in survey's outcomes. For instance, water with demand, allocate resources more equitably, and ultimately con- quality should be bacteriologically tested (Howard et al., 2003,draft; ceive and implement policies with a focus on poverty reduction Jiménez and Pérez-Foguet, 2012; Joint Monitoring Programme, 2011); (Crook, 2003; Devas and Grant, 2003; Steiner, 2007). To effectively while study of handwashing through structured observation may help do this, local governments need to make evidence-based decisions, avoid over-reporting of “desirable” hygiene behaviours (Manun'Ebo et which primarily depend on the availability of accessible, accurate al., 1997). and reliable data that are routinely collected, disseminated and Finally, there is an issue with the statistical precision of the esti- updated. Amongst others, these data may be employed to i) measure mates. A common monitoring need in local decision-making is to as- progress and performance, ii) improve transparency in budgetary sess separately the performance of the lowest administrative subunits procedures and promote increased investments in the sector, and (e.g. communities, villages, etc.) in the area of interest (e.g. district, iii) allocate resources to deliver services where they are most needed. municipality, etc.). Since the number of these administrative subunits Today, reliable information on key indicators at local level often lacks, is generally large, the level in which information needs to be but even when it is available, the uptake for such data by policymakers disaggregated is high, and one is therefore faced with the need to bal- is, at best, challenging (WaterAid, 2010). Limited capacities of recipient ance precision against cost when deciding the size of the sample governmental bodies, inadequate sector-related institutional frame- (Bennett et al., 1991; Grosh, 1997; Lwanga and Lemeshow, 1991). work, and lack of data updating mechanisms are common reasons Moreover, a scientifically valid sampling methodology is necessary that hamper an adequate appropriation and continued use of the data to achieve reliable estimates. For household surveys, a cluster sam- for planning and monitoring purposes (Joint Monitoring Programme, pling design has proved a practical solution (Bennett et al., 1991; 2011; World Health Organization, 2012). Lemeshow and Stroh, 1988; United Nations Children's Fund, 2006). In an effort to address one of the shortcomings cited above, i.e. the And water point mapping exercises, where a comprehensive record lack of reliable data, this study deals with the design of adequate meth- of water sources is undertaken (i.e. no sampling), have also been suc- odologies for routine data collection. A variety of tools and techniques cessfully implemented to monitor the distribution and status of water have been developed in recent years to collect primary data for the supplies (WaterAid, 2010). WASH sector. Amongst others, the Water Point Mapping –WPM– In sum, a need for further research into feasible alternatives for (WaterAid and ODI, 2005), the UNICEF-supported Multiple Indicator data collection to the currently used strategies has been highlighted Cluster Survey –MICS– (United Nations Children's Fund, 2006), the (Joint Monitoring Programme, 2011), and the purpose of this study Rapid Assessment of Drinking Water Quality –RADWQ– (Howard et al., is to present a new specific approach for the WASH sector at local 2003, draft), and the Water Safety Plans (Bartram et al., 2009). However, level, as an attempt to overcome previous shortcomings. It takes the methodological problems arise when they are implemented at local scale WPM as a starting point to record all available water sources at a par- to produce reliable inputs for planning support. ticular location, which results in the need of covering the whole area First critical shortcoming is related to the type of data required to of intervention. This information is then combined with data provid- monitor the sector, since different information sources may be required ed from a household-based survey, in which a representative sample (Joint Monitoring Programme, 2012b). Household surveys are by of households is selected to assess sanitation and hygiene habits. In large the most commonly used tools for collecting WASH data (Joint brief, taking advantage of the current momentum of WPM as field Monitoring Programme, 2006; Macro International Inc., 1996; United data collection method in the water sector (Government of Liberia, Nations Children's Fund, 2006).Butafocusonhouseholdsisnotsufficient 2011; Government of Sierra Leone, 2012; Jiménez and Perez-Foguet, to answer many relevant questions, and hence needs to be supplemented 2011; Pearce and Howman, 2012; WaterAid, 2010) and the growing with data from other sources. For instance, an audit at the water point interest among development stakeholders in harmonizing sector might provide insight into operational and management-related aspects monitoring (Joint Monitoring Programme, 2012c), this study suggests of the service. A methodology to efficiently combine these two types of in- a cost-efficient alternative to simultaneously perform a WPM togeth- formation sources should have potential for wider implementation. er with a household survey, thus producing a comprehensive WASH Another key limitation is that of comparability (Joint Monitoring database as a valuable output for policymaking. To test the applicabil- Programme, 2006), since a variety of indicators are being simulta- ity and validity of the proposed approach, three different case studies neously employed to measure different aspects of the level of service. in East Africa are presented. More often than not, to assess trends over periods of time or to com- In Section 2, basic concepts of the evaluation framework pare indicators regionally has therefore remained challenging. As a employed in this study are outlined. The methodology proposed to first step against this comparability problem, the Joint Monitoring collect WASH primary data is described in Section 3. It presents the Programme for Water Supply and Sanitation (JMP) formulated a set of three case studies and highlights key features of the approaches harmonized survey questions (Joint Monitoring Programme, 2006)to adopted in each one of them. Section 4 computes statistical validity provide worldwide reliable estimates of drinking-water and sanitation of the method and, in so doing, provides useful guidelines on data ex- coverage at national level (Joint Monitoring Programme, 2012a). In ploitation for decision-makers. Integral to this discussion there are a so doing, JMP has improved the processes and approaches to monitor- variety of alternatives to disseminate achieved results, in an effort ing the sector, though the definitions employed have been criticised to provide clear and accurate policy messages. The paper concludes as being too infrastructure-based. (Giné-Garriga and Pérez-Foguet, that efficient data collection mechanisms can be designed to produce under review-b; Giné-Garriga et al., 2011; Hunt, 2001; Jiménez and reliable estimates for local planning processes. Their implementation Pérez-Foguet, 2012). Today, an ongoing consultative process is debating in the real world, however, is to a certain extent elusive; and specific a consolidated proposal of targets and indicators for the post-2015 challenges that remain unaddressed are pointed out as ways forward. monitoring framework (Joint Monitoring Programme, 2011; Joint Monitoring Programme, 2012c). 2. Evaluation framework The techniques employed for data acquisition also play a key role in terms of data reliability and validity (United Nations Children's Fund, This section introduces core aspects of the evaluation framework pro- 2006). A well-designed questionnaire helps elicit a response that is posed to locally assess the WASH status. First, the two methodologies 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. the (i.e. all enumeration areas as communities, villages, etc.). Taking ad- Water Point Mapping (WPM) and the Multiple Indicator Cluster Survey vantage of this logistic arrangement, and in addition to the mapping, (MICS). Second, it discusses the issue of the sample size, as the survey de- a household-based survey may be thus designed to evaluate sanita- sign has to enable the compilation of accurate primary data to produce tion and hygienic practices at the dwelling. As it may be assumed statistically representative estimates. Third, a reduced set of measurable that all households are located within walking distance of one water indicators is proposed as the basis of the monitoring strategy. 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 2.1. The Water Point Mapping on the MICS, i.e. a methodology developed by UNICEF (United Nations Children's Fund, 2006) to collect social data, which is ultimately Mapping of water points has been in use by NGOs and agencies world- required amongst others for monitoring the goals and targets of the wide for over a decade, particularly in sub-Saharan Africa (e.g. Malawi, Millennium Declaration or producing core United Nations' develop- Tanzania, Ghana, Ethiopia, Zambia, Liberia, Sierra Leone, etc.). This meth- ment indices. The study population is stratified into a number of small odology, largely promoted by the NGO WaterAid, can be defined as an mutually exclusive and exhaustive groups, so that members of one ‘exercise whereby the geographical positions of all improved water group cannot be simultaneously included in another group. In this points1 in an area are gathered in addition to management, technical study, however, main difference is that when sampling, a sample of and demographical information’ (WaterAid and ODI, 2005). WPM in- households is selected from each stratum (stratified sampling), rather volves the presentation of these data in a spatial context, which enables than selecting a reduced number of strata, from which a subsample of a rapid visualization of the distribution and status of water supplies. households is identified (cluster sampling). In so doing, the risk of A major advantage is that water point maps provide a clear message on homogeneity within the strata remains relatively low, thus reducing who is and is not served; and particularly in rural areas, they are being the need for applying any correction factor in sample size determina- used to highlight equity issues and schemes' functionality levels at and tion, i.e. the “design effect2”.A“design effect” of 1 is accepted in strati- below the district level. This information can be employed to inform fied random sampling, though ten-fold or even higher variations are decentralized governments about the planning of investments to increase not uncommon values in cluster samplings with large cluster's sizes water coverage (Jiménez and Pérez-Foguet, 2010; WaterAid, 2010). (Kish, 1980). In a WASH cluster survey, a value of 4 may be appropriate Specifically, the mapping does not refer to a fixed set of indicators, as acknowledged by the United Nations Children's Fund (2009). and two different actions are suggested in this regard: i) biological testing to ensure water quality; and ii) the inclusion of unimproved 2.3. Sample size and precision sources. First, water quality analysis has long been nearly absent from water coverage assessments because of affordability issues In local decision-making, of interest is the evaluation of the level (Howard et al., 2003, draft; Joint Monitoring Programme, 2010). In of service for the recipient administrative unit as a whole. However, the absence of such information, it is assumed that certain types of acknowledging that administrative subunits may have uneven cover- water supplies categorized as ‘improved’ are likely to provide water of age, there is also concern for estimating their performance to identify better quality than traditional unimproved sources (Joint Monitoring the most vulnerable areas. In other words, one regional coverage Programme, 2000; Joint Monitoring Programme, 2012a). This assump- value might be sufficient from the viewpoint of central governments; tion, though, appears over-optimistic, and improved technologies do but since such value says nothing about local variations, estimates at not always deliver safe water (Giné Garriga and Pérez-Foguet, under the lowest administrative scale are required for decentralised plan- review-b; Jiménez and Pérez-Foguet, 2012; Sutton, 2008). Contrary to ning. To produce local robust estimates substantially increases the re- what might be expected, and particularly in comparison with overall quired size of the sample, which directly affects the cost of the survey. investments projected for new infrastructure or with ad hoc quality The goal of WPM is to develop a comprehensive record of all water testing campaigns, water quality surveillance does not significantly points available in the area of intervention. There is thus no need of impact on the overall cost of the mapping exercise: from USD 12 to 15 sampling. For the household survey, in contrast, a statistically repre- dollars/waterpoint in standard WPM (Stoupy and Sugden, 2003)upto sentative sample needs to be selected. The basic sampling unit is USD 20 when quality testing is included (Jiménez and Pérez-Foguet, the household, and the size of a representative sample n is numerical- 2012). Second, being the original focus of WPM on improved waterpoints, ly given by Cochran (1977, third edition): unimproved sources may be also mapped if they are accessed for domestic purposes. A thorough analysis of collected data would shed light on 2 z −α= pðÞ1−p D the suitability of the improved/unimproved classification proposed by ¼ 1 2 ð Þ n 2 1 the JMP, but more importantly, this would help understand equity issues d in service delivery (Giné-Garriga and Pérez-Foguet, under review-b; where: Jiménez and Pérez-Foguet, 2011; Joint Monitoring Programme, 2012a). α is the confidence level, and z is a constant which relates to the 2.2. Household survey normally distributed estimator of the specified level. For a confidence level of 95% (α = 0.05), the value of z1 − α/2 is 1.96 α α A major strength of WPM is, per definition, comprehensiveness (z1 − α/2 =1.64when is 0.1; z1 − α/2 =1.28when is 0.2); with respect to the sample of water points audited, which entails p is the assumed proportion of households giving a particular re- complete geographic representation of all strata in the study area sponse 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 =1instratified random sampling. However, acknowledging that a com- 1 The types of water points considered as improved are consistent with those accepted internationally by the WHO/UNICEF Joint Monitoring Programme Joint Moni- plete random exercise for household selection is almost toring 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 Peo- 2 The “design effect” is an adjustment that measures the efficiency of the sample de- ple without Access to Safe Water by 2015 – A Malawian Perspective. Part 2: New indi- sign, and is calculated by the ratio of the variance of an estimator to the variance of the cators for the millennium development goal. WaterAid, London, 2003. same estimator computed under the assumption of simple random sampling. R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711 703

unachievable in each stratum, a value of 2 is recommended. It 2006; Joint Monitoring Programme, 2012c). Finally, indicators should is noteworthy that in comparison with the sampling plan be EASSY (Jiménez et al., 2009): Easily measurable at local level, required in a standard cluster survey, where D = 4 (United Accurately defined, Standardized and compatible with data collect- Nations Children's Fund, 2009), the sampling approach ed elsewhere, Scalable at different administrative levels, and Yearly adopted in this study halves the sample size, which consider- updatable. In Table 2, a short list of core indicators is summarized. ably reduces the overall cost of the data collection exercise; Moreover and beyond average attainments, it is accepted that any and evaluation framework should identify the high-risk groups in which d is the required precision on either side of the proportion. A policy-makers may prioritize efforts and resources (Joint Monitoring typically used figure in similar surveys is d = ±0.05, based Programme, 2012c). The concern is to identify gaps in WASH out- on the argument that lower precision would produce comes between the poor and the better off. To do this, one option is unreliable results while a higher precision would be too to employ “direct” measures of living standards, such as household expensive as it would require a very large survey. This pre- income or expenditure, though they are often unreliable (Filmer cision may be considered at highest scale of intervention. and Pritchett, 2001). Another approach is to use a “proxy” measure, Estimates at lower administrative scale should be assessed such as a wealth index constructed from information on household own- with lower precision; i.e. d = ±0.10 or ±0.15. 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- As an example, a minimum sample size n of192wouldberequiredto hold head, etc. These data promote evidence-based pro-poor planning produce estimates in each administrative subunit within 20% (±10%) of and targeting processes, and ultimately help improve service level of thetrueproportionwith95%confidence (D =2).Ifthesampledesign the most vulnerable. 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 Eq. (1) is valid where populations are at 3. Method least medium-size (N > 100). In contrast, when applied to more reduced populations, it produces unachievable figures or large sampling As abovementioned, the approach adopted for data collection errors. For use in local household-based surveys, Giné-Garriga and combines a mapping of water sources with a stratified survey of Pérez-Foguet (under review-a) proposed an alternative approach to households. Different methodologies exist which combine the determine the sample size, in which the practitioner may easily identify waterpoint and the household as key information sources, but they the sampling plan that best balances precision and cost (Table 1). 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–. 2.4. Water, sanitation and hygiene indicators Key features of the proposed methodology include i) an exhaustive identification of enumeration areas (administrative subunits as com- A core element of any evaluation framework is the set of indicators in munities, villages, etc.); ii) audit in each enumeration area of all im- which base the analysis. It is evident that from a WASH perspective, proved and unimproved water points accessed for domestic a wide range of variables exists to assess the current status of service purposes; and iii) random selection of a sample size of households level. Of particular interest is the recently adopted human rights frame- that is representative at the local administrative level (e.g. district, work, which reflects the concept of progressive realization in the level municipality, etc.) and below. of service and requires the definition of specific indicators to deal with The mapping of waterpoints is exhaustive regardless functionality is- the issues of affordability, quality, reliability and non-discrimination, sues, though the inclusion of unimproved sources in the analysis will be amongst others; or the debate guided by WHO and UNICEF about the dependent on the scope of the exercise and available resources. The need post-2015 monitoring of WASH (Joint Monitoring Programme, 2011, to tackle equity issues has been highlighted from the viewpoint of 2012c). To exactly identify what should be measured remains challenging, human rights, and any exercise covering unimproved waterpoints though, and rules of thumb for the selection process include the following would provide inputs to elucidate the access pattern of the population. criteria. First, in terms of efficiency, the number of indicators should be as In rural contexts, however, this type of water source may be common, reduced as possible but sufficient to ensure a thorough description of the thus increasing significantly the budget and resources devoted to data context in which the service is delivered (Joint Monitoring Programme, collection in case of inclusion. Similarly, where main water technology 2011; United Nations Children's Fund, 2006). Second, to resolve the is piped systems with household connections, the idea of a comprehen- comparability problems, survey questions need to be harmonized sive audit of all these private points-of-use is practically impossible. with those internationally accepted (Joint Monitoring Programme, A more convenient solution would be to visit the distribution tank and

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

N α = 95% α = 90% α = 80%

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

8 –– 76–– 76– 765 10 – 987– 987– 976 15 14 13 11 9 14 12 10 8 14 11 9 7 20 18 16 13 10 18 14 11 9 17 13 10 7 25 22 18 14 11 21 16 12 9 19 14 10 50 36 26 18 13 33 22 15 11 28 18 75 46 30 21 14 40 25 17 32 100 53 33 22 15 46 27 17 35 150 64 37 23 53 29 39 250 75 40 24 60 500 87 43 67 Eq. (1) 96 43 24 15 67 30 17 11 41 18 10 7 704 R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711

Table 2 List of core WASH indicators.

WASH component Indicator Rationale Source of Technique information

Water Access to improved % households with access to improved Core water-related indicator. An improved source serves Household Direct questioning supply water sourcesa, b water supply as a proxy indicator for whether a household's drinking-water is safe. % of households adequately covered To geographically show the least covered administrative Waterpoint Visit to all (based on the standard source:man subunits, i.e. with less number of water points compared waterpoints ratio) to the population living there. One way distance to % of households spending, on average, To assess whether the source is sufficiently close to the Household Direct questioning water source (km) a, b more than 30 min in fetching water 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 % households in which women shoulder This information helps identify gender and generational Household Direct questioning watera the burden in collecting water disparities with respect to water-hauling responsibilities. It also ascertains who would profit from bringing water closer to households. Domestic water Average rate of per capita domestic Distance to the water source may be an indirect indicator Household Direct consumption water consumption (based on of the of water use, but it is not accurate enough to draw questioning/ number of containers consumed per day conclusions. From the health viewpoint, it is important to observation and the rough volume of these determine whether the volume of water collected for basic containers) needs reaches the minimum target value. Operational status % functional water points To highlight sustainability issues, i.e. to identify operation Waterpoint Observation of water sourceb and Maintenance (O&M) problems and to assess the overall quality of the O&M system. Water quality % bacteriological acceptable water To evaluate water safety, specifically to determine Waterpoint Water quality (bacteriological sources presence of faecal coliforms and few other critical testing contamination)b parameters (pH, conductivity, turbidity and nitrates) (portable kit) Seasonality of water % year-round water sources To identify seasonal or intermittent supplies, and to help Waterpoint Direct questioning resourcesb 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 Management % facilities with a functional and A key sustainability aspect of the supply. For successful Waterpoint Direct questioning systemb registered water committee and sustainable water schemes management, a proper institutional setting is required, and at least functional water user committees need to be established. Maintenance system % facilities with local access to technical A key sustainability aspect of the supply. Access to skills Waterpoint Direct questioning skills and spare parts and spares promotes locally-based maintenance. Financial control % of facilities in which at least 1 meeting A key sustainability aspect of the supply, particularly Waterpoint Direct questioning was held during last year to discuss related to improve transparency and accountability. income and expenditure (both with the Regular meetings are proxies of upward and downward community and the local authority) 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 % water entities which exempt A key community crosscutting issue. Fundamental human Waterpoint/ Direct questioning delivery vulnerable houses from paying for water rights criteria include accessibility, affordability and household non-discrimination. Sanitation Access to and use of % households with access to improved Core water-related indicator. Important to check the cur- Household Observation sanitation sanitationa, b rent use of the facility, rather than mere household's ownership of the toilet. % households practicing open defecationb To help distinguish between open defecation and latrine Household Observation sharing, since both practices are categorized as unimproved in JMP figures. % households sharing improved The shared status of a sanitation facility may entail poorer Household Direct questioning/ sanitation facilitiesa hygienic conditions than facilities used by a single household. observation Latrine conditions % latrines maintained in adequate Regardless the category of the toilet, it is important to Household Observation sanitary conditions 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. Hygiene Handwashing % latrines with appropriate handwashing A rigorous assessment of handwashing behaviour would Household Observation deviceb device 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 % households with adequate water Household Direct questioning/ treatmenta treatment observation Disposal of % child caregivers correctly handling Children's faeces are the most likely cause of faecal Household Direct questioning children's stoolsa, b baby excreta contamination to the immediate household environment.

a JMP indicator. b Indicator proposed by JMP for Post-2015 monitoring of drinking-water, sanitation and hygiene. R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711 705 a reduced number of domestic taps, which are taken as representative of quality control procedures must be in place to ensure that the data the overall system. reflects the true position as accurately as possible, and routine analy- The household survey is conducted in parallel with the mapping. sis of database or random checks of a reduced number of question- Ideally, a defined number of households will be selected in a statisti- naires may help detect data inconsistencies and improve database cally random manner from a comprehensive list of all households robustness. 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 3.1. Study area a list by carrying out a quick census. In those cases where enumerat- ing all households is impracticable, literature suggests different sam- Three different East African settings were selected as initial case stud- pling techniques to achieve a random or near-random selection ies to test the applicability and validity of the proposed methodology, (Bennett et al., 1991; Frerichs and Tar, 1989; Lemeshow and Stroh, namely the district of Kibondo (Tanzania, in 2010), the district of Homa 1988). They usually involve two stages: the identification of one or Bay (Kenya, in 2011) and the municipality of Manhiça (Mozambique, in various households to be the starting point, and a method for 2012). The implementation of each case study adopted particular fea- selecting “n” successive households, preferably spread widely over tures, which are briefly summarized in Table 3, and scope of work was the community. In the end, where a complete random exercise is not designed on the basis of local needs (e.g. inclusion/exclusion of unim- achievable, any methodology during the sampling process which pro- proved waterpoints, visit to schools and health centres, the focus and motes that the sample is as representative as possible would be accept- level of detail required in survey questionnaires, etc.). However, they all able, as long as it is clear and unambiguous, and does not give the shared same approach, method and goals: i) they were formulated enumerator the opportunity to make personal choices which may intro- against specific call from a development-related institution to support duce bias. In these cases, however, and to ensure data validity, to apply a local level decision-making (in Tanzania, a Spanish NGO; in Kenya, correction factor in sample size determination (D = 2) is recommended. UNICEF; and in Mozambique, the Spanish Agency for International In terms of technique, the method relies on a variety of mecha- Development Cooperation); ii) the Research Group on Cooperation and nisms to assure quality of produced outcomes. Among the most im- Human Development at the Technical University of Catalunya undertook portant are: overall coordination of the study; iii) the local authority was engaged as principal stakeholder throughout the process; and iv) a consultancy – Territorial delimitation of study area. As an exercise to support firm was contracted for field work support. planning, administrative subunits in which base data collection should play a relevant administrative role in decentralised service 4. Discussion delivery. Thus, they should be adequately delimited, unambiguous and well-known by both decision-makers and local population. In previous section, a simplified approach to survey design for – Design of survey instruments. On the basis of a reduced set of reli- WASH primary data collection has been outlined. The goal of the able and objective indicators (Table 2), appropriate survey tools discussion first focuses on providing statistical robustness of the should be developed for an accurate assessment of the WASH sta- methodology. To do this, we compute basic statistical parameters, in tus. This study is reliant on a combination of quantitative and which also base the definition of criteria that will help validate the qualitative study tools, which are specially designed to collect collected data from the viewpoint of decision-making. Second, and data from the water point and the household. Field inspections with the aim of communicating clear messages to policy-makers, a at the source employ a standardized checklist to evaluate the exis- number of alternatives to disseminate achieved results are presented. tence, quality and functionality of the facility; and a water sample is also collected for on-site bacteriological testing. At the dwelling, 4.1. Estimating the precision of a proportion information related to service level is captured through a structured interview administered to primary care-givers. In addition, direct The data collected at the dwelling, because of the sampling strate- observation enables a complementary evaluation of domestic gy employed for households' selection, require statistical validation. hygiene habits that may not be otherwise assessed, as sanitary con- The ultimate goal is to guarantee reliability of any outcome produced ditions of the latrine, existence and adequacy of the handwashing fa- and thus avoid decisions based on false or misleading assumptions. To cility, etc.. this end, estimates of proportions may be calculated together with pre- – Involvement and participation of local authorities. This study engages cision of those estimates, so that confidence intervals can be assessed. in various stages of the process with those government bodies with As shown, all calculations described below are simple and can be easily competences in WASH. Specifically in data collection, the commit- computed in any standard spread-sheet. ment of officers belonging to the local government i) helps ensure The proportion p, for example, of households in the ith subunit alinkbetweenfield workers and the local structures at community with access to improved sanitation is given by Eq. (2): level, and ii) promotes sense of ownership over the process, as pre- requisite for incorporating the data into decision-making. As important of promoting collaborative data collection methods is to foresee ¼ yi ð Þ pi 2 the viability of future data update activities, and accessibility and re- ni liability 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 where: particular context. Finally, data collection focuses on the administrative scale in which decisions are based, thus producing relevant in- yi number of households in the ith subunit with access to formation for local policy-makers. improved sanitation; and – Pilot study. A pilot run helps explore the suitability of the approach ni number of surveyed households in the ith subunit. adopted, i.e. methodology and study instruments. Further fine- tuning (question wording and ordering, filtered questions, deletion When estimating the proportion at the overall administrative unit, of pointless questions, etc.) follows the pilot. population size of subunits should be taken into consideration to – Data processing: The data entry process needs to be supervised, and avoid subunits' under or overrepresentation. Therefore, achieved re- the produced datasets need to be validated on a regular basis. Various sponses should be weighted in proportion to the actual population of 706 R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711

Table 3 Key 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 13.578 P (42%), 986 IWPs 3.656 (20 wards) T (45%) and OC (13%) − 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 domestic hygiene issues (10 min 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. Homa Bay, Kenya District 32.389 P (74%), T (17%) 255 187 IWPs 1.157 − The total area is 1,169.9 km2, and the total population is about 366,620 (5 divisions) and OC (9%) and 68 UWPs (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 min per WP) + 1 water quality test. − HH checklist included 65 questions related to water, sanitation and domestic hygiene issues (35 min 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. Manhiça, Municipality 23.719 P (41%), T (42%) 228 224 IWPs 1.229 − The total area is 250 km2 and the population is estimated at 57,512 Mozambique (18 bairros) and OC (17%) and 4 UWPs (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 min per WP) + 1 water quality test − HH checklist included 82 questions related to water, sanitation and domestic 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 e Saneamento (partially involved), 14 staff from a consultancy firm and 1 people 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, 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.

each subunit. To compute the confidence limits for pi, we use the F dis- on practical grounds, estimates of only few indicators are shown tribution (Leemis and Trivedi, 1996), i.e. the so called Clooper–Pearson herein. interval (Reiczigel, 2003): In decision-making and specifically to support targeting, one would opt to employ the proportion and the confidence interval for ! − − þ 1 a given variable to rank all the administrative subunits (Giné-Garriga ¼ þ n y 1 ð : Þ pL 1 3 1 and Pérez-Foguet, under review-a), where top positions would yF2y;2ðÞn−yþ1 ;1−α 2 denote highest priority. From Table 4,forinstance,Rangwecould be easily identified as the most water poor division in Homa Bay ! −1 − (pi, access = 0.355; pi, time = 0.753), in which thus focus policy ¼ þ n y ð : Þ pU 1 ðÞþ 3 2 attention. y 1 F2ðÞyþ1 ;2ðÞn−y ;α 2 Such prioritization, however, remains elusive where confidence intervals of the different subunits overlap. As general rule, it can where: be seen (Tables 4 to 7) that lower levels of confidence (α increases) give smaller confidence intervals, and hence reduced overlapping. fi pu and pl are the upper and lower limits of the con dence interval; Therefore, decision-makers would need to balance precision of final and estimates (α) against robustness of statistics for planning purposes. α is the confidence level. In this exercise, two different confi- For example, in Homa Bay and as regards time spent in water dence levels have been employed for calculation purposes, hauling (Table 4), one could target differently Riana (pi = 0.910) i.e. 90% (α = 0.1) and 80% (α = 0.2). and Nyarongi (pi = 0.936) for confidence level of 80% (0.882– 0.932 and 0.913–0.953 respectively), though such discrimination Summary of aforementioned statistics (proportion and confidence would not be feasible with 90% confidence because of overlapping interval) for the survey variables (listed in Table 2) are presented in of the proportions with their corresponding intervals (0.875–0.938 Tables 4 and 5 –Kenya–,6–Tanzania– and 7 –Mozambique–, though and 0.906–0.957 respectively). R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711 707

Table 4 Table 6 Estimated proportion and conﬁdence interval of water-related indicators in Homa Bay Estimated proportion and conﬁdence interval of WASH indicators in Kibondo District (Kenya). (Tanzania).

Access to improved waterpoints Time to fetch watera Use of Sanitation Latrine Conditions α = 0,1 α = 0,2 α = 0,1 α = 0,2 α = 0,1 α = 0,2 α = 0,1 α = 0,2 Ward pi pi pi pi pL,i -pU,i pL,i -pU,i pL,i - pU,i pL,i -pU,i p - p p - p p - p p - p L,i U,i L,i U,i L,i U,i L,i U,i Bunyambo 0,017 0,004 - 0,043 0,006 - 0,037 0,144 0,101 - 0,194 0,109 - 0,183 Asego 0,510 0,449 - 0,569 0,462 - 0,556 0,807 0,755 - 0,851 0,766 - 0,842 Busagara 0,049 0,025 - 0,083 0,029 - 0,075 0,259 0,206 - 0,317 0,217 - 0,305 Rangwe 0,355 0,298 - 0,414 0,310 - 0,401 0,753 0,696 - 0,802 0,708 - 0,792 Gwunumpu 0,021 0,007 - 0,047 0,009 - 0,041 0,086 0,054 - 0,127 0,060 - 0,117 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 Itaba 0,072 0,043 - 0,112 0,048 - 0,103 0,133 0,093 - 0,182 0,101 - 0,171 Riana 0,441 0,388 - 0,493 0,399 - 0,482 0,910 0,875 - 0,938 0,882 - 0,932 Kakonko 0,036 0,016 - 0,066 0,019 - 0,059 0,087 0,056 - 0,127 0,062 - 0,118 Note: a) Households spending less than 30 min for one round-trip to collect water. In colour Kasanda 0,043 0,021 - 0,076 0,025 - 0,069 0,081 0,050 - 0,122 0,056 - 0,113 – – ﬁ α (red orange green), prioritization groups based on con dence intervals ( =0.2). 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

One factor that challenges a reliable prioritization rank is the pop- Kizazi 0,029 0,011 - 0,059 0,013 - 0,052 0,149 0,105 - 0,201 0,113 - 0,190 ulation variability in and within different administrative subunits. Kumsenga 0,013 0,002 - 0,039 0,003 - 0,033 0,057 0,029 - 0,096 0,034 - 0,087 Depending on the nature of the indicator, a homogeneous pattern in Mabamba 0,017 0,004 - 0,042 0,006 - 0,036 0,139 0,098 - 0,188 0,106 - 0,177 the study area becomes more evident, primarily by i) reduced lengths Misezero 0,055 0,030 - 0,088 0,035 - 0,081 0,229 0,180 - 0,282 0,190 - 0,271 of confidence intervals, and ii) greater overlapping of interval esti- Mugunzu 0,000 0,000 - 0,016 0,000 - 0,012 0,098 0,063 - 0,143 0,070 - 0,133 mates. More specifically, the data from the three case studies show Muhange 0,000 0,000 - 0,014 0,000 - 0,010 0,086 0,056 - 0,124 0,061 - 0,115 that indicators with low variability include gender disparities in the Murungu 0,035 0,015 - 0,068 0,018 - 0,061 0,097 0,061 - 0,143 0,068 - 0,133 burden of collecting water and point-of-use water treatment; while Nyabibuye 0,028 0,011 - 0,057 0,013 - 0,050 0,094 0,061 - 0,138 0,067 - 0,128 at the other end of the spectrum, indicators presenting marked re- Nyamtukuza 0,009 0,001 - 0,029 0,002 - 0,024 0,047 0,025 - 0,077 0,029 - 0,071 gional disparities are access to improved water supplies, and time Rugenge 0,006 0,000 - 0,026 0,000 - 0,021 0,067 0,038 - 0,105 0,043 - 0,097 spent in fetching water. It might be concluded from the data that Rugongwe 0,029 0,012 - 0,055 0,015 - 0,049 0,206 0,160 - 0,257 0,169 - 0,246 the local scale of analysis do not add value where domestic habits Note: In colour (red–orange–green), prioritization groups based on confidence intervals and practices tend to a homogeneous behaviour, which would (α =0.2). 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/ For instance in Kibondo (Table 5), four different prioritization groups unimproved classification of water supplies provides misleading in- may be easily defined with regard to latrines' sanitary conditions, formation. Since the vast majority of households access an improved which ultimately allows a transparent identification of those subunits source, the level of service is better described through the availability with poorest hygiene behaviour. Based on the same approach and to as- of home connections (Table 7). As regards sanitation, it is gleaned sess use of improved sanitation facility in Manhiça (Table 7), five target from the estimates of Tables 5 and 6 that the approach adopted by groups could be established. the JMP does not lead to an obvious discrimination among adminis- As regards the information provided by the waterpoint mapping, the trative subunits in rural areas as Homa Bay and Kibondo, while an analysis may focus on availability and geographic distribution of indicator related to open defecation status provides a clearer picture. waterpoints. Without adequate combination of demographic data, In Manhiça, in contrast, the opposite applies (Table 7). In sum, where however, this information might be misleading. Hence access indicators the studied variable shows a strong homogeneous pattern, the search are usually assessed on the basis of standard assumption on the number for alternative indicators may be appropriate, since a larger sample of users per water source (i.e. the source:man ratio, which in Kenya size will probably prove ineffective to distinguish between different stands at 250 people per public tap). Two different conclusions might population groups. be drawn from Table 8. First, it is observed that coverage levels of im- Despite the abovementioned restrictions, it is noteworthy that sta- proved water points at the household (see Table 4) or at the waterpoint tistics shown in Tables 4 to 7 provide accurate inputs to policy-makers are substantially different; i.e. the standard source:man ratio is not for the purpose of targeting. On the basis of performance level and followed up in practice. Second, access is dependent on the level of therefore taking the estimates of proportions as reference points, for a service; e.g. one out of five families in Homa Bay (21,1%) may get given indicator one could group all subunits in different clusters, in drinking water from an improved source, though this ratio is halved such a way as to avoid overlap of their respective confidence intervals. when water quality issues are taken into consideration.

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 Division α = 0,1 α = 0,2 α = 0,1 α = 0,2 α = 0,1 α = 0,2 pi pi pi pL,i - pU,i pL,i - pU,i pL,i - pU,i pL,i - pU,i pL,i - pU,i pL,i - pU,i 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 Ndhiwa 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 Nyarongi 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 Riana 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). 708 R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711

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

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 Bairro pi pi pi 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)

4.2. Communicating clear messages to policymakers and within different administrative units; and mapping permits a feasible visualization of such heterogeneity (Davis, 2002). In addition, it provides a The ultimate goal of sound sector-related data is to improve decision- means for integrating data from different sources and from different dis- making. To do this, two elements are necessary (Grosh, 1997): the data ciplines (Henninger and Snel, 2002), which helps provide a complete pic- must be analyzed to produce outcomes that are relevant to the policy ture of the context in which the service is delivered. In the end, mapping question, and the analysis must be disseminated and transmitted to comes out an appropriate dissemination tool for sector planning, moni- policymakers. Unless data is easily accessible and is presented in a toring and evaluation support. user-friendly format, decision makers will commonly do without the in- The map in Fig. 1, for example, shows the spatial distribution of im- formation. This section thus attempts to present a set of survey outputs proved water sources in Homa Bay, and highlights the issues of func- to demonstrate that the approach adopted in this study produces perti- tionality and seasonality. It is observed that the majority of audited nent sector data, which adequately exploited and disseminated might be sources were found operational and with no seasonality problems employed by policy planners in decision-making processes. (71%), despite regional disparities. If such point-based information is To begin, water and sanitation poverty maps are powerful instru- combined with demographic data and the source:man ratio cited ments for displaying information and enable non-technical audiences to above, a coverage density map can be developed (Fig. 2)toshowacces- easily understand the context and related trends (Henninger and Snel, sibility rather than availability aspects. It may be gleaned from the map 2002). As observed from the tables discussed above, WASH-related pov- that, on average, only 8.7% of population are properly served by func- erty may follow a highly heterogeneous pattern, widely varying between tional and year-round improved waterpoints, i.e. the percentage of

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

Division Pop (2011) No. imp WPs Funct WPs No. unim WPs Unserved pop (by IWP) IWPDa FIWPDb YR — FIWPDc BS — FIWPDd

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%

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. R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711 709

Functional and year round As seen in Table 9, one different ranking is produced depending on each abovementioned criteria, showing both ranks poor correlation. For Functional but seasonal example, it is observed that Riana is prioritized as its open defecation- fi Non-functional index stands at 67%, although in terms of potential bene ciaries, 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 wherecoverageislower(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 (pHB = 0.812 and pM = 0.14 respectively). A more in-depth analysis shows, however, that piped water on premises Fig. 1. Distribution of functional improved water points, at location level. is enjoyed mainly by the wealthiest (pM = b0.001), while the “poor” significantly spend more time spent in hauling water (pHB =0.016 and pM b 0.001). As regards sanitation, it is noted that use of improved latrines is positively related to wealth (pHB b 0.001 and pM = b0.001). population covered if it is assumed that each community tap only serves And for instance, the richest 25% of the population is almost ten 250 people. (Homa Bay) or six (Manhiça) times as likely to use an improved san- Another concern in local decision-making is more related to the itation facility as the poorest quartile, while the poorest 25% is two/ lack of transparent mechanisms to establish needs and priorities. twenty times more likely to practise open defecation than the Ideally, the most vulnerable segments of the population should be richest quartile. Much like water supply and sanitation, considerable precisely targeted and then recipient of policy attention and public differences are also found regarding to hygiene practices between resources. And for this purpose, rankings and league tables are pow- the rich and the poor. The percentage of households with adequate erful instruments. To denote priorities and specifically to define prior- point-of-use water treatment increases with wealth status (pHB = itization criteria, two different approaches may be adopted. In terms 0.043 and pM = b0.001). And socio-economic status of the household of regional equity, the goal would be to reach a minimum coverage shows strong association with safety in disposal of children's faeces threshold in every administrative subunit. But based on an efficiency (pHB =0.028andpM = 0.007). criterion, those subunits with highest number of potential beneficia- fi ries should be rst targeted, regardless of coverage. A combination 5. Conclusions of both criteria would also be feasible, despite resulting in a complex indicator. 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 pros- < 5% pect to develop more pro-poor policies, though, depends upon real 5 - 10% efforts to strengthen the capacity of decentralised authorities. Integral 10 - 15% to this challenging process, and to enable policymakers to move from opaque to informed discussions, accurate and reliable data at local 15 - 25% level have to be accessible, i.e. routinely collected and adequately Equal or more than 25% disseminated. Against this background, the aim of this article is to develop a cost-effective method for primary data collection which ultimately No Funct Imp WP 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 sam- Fig. 2. Density of year round and functional improved waterpoints, at location level. ple size determination. 710 R. Giné-Garriga et al. / Science of the Total Environment 463–464 (2013) 700–711

Table 9 Priority 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 Rank A' (equity) No served population Rank B (efﬁciency) 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

Second, the analysis of data in the discussion has produced valuable de Catalunya) through the Beatriu de Pinos grant (BP-DGR 2011) to outputs that might be further exploited for local level policymaking Dr A. Jiménez is also acknowledged. support. It has shown how data can feed into planning and targeting decisions in a range of different ways. However, to offer relevant References guidance to the policy question, the analysis must be disseminated effectively. Maps or simple thematic indices are adequate tools to cap- Bartram J, Corrales L, Davison A, Deere D, Drury D, Gordon B, et al. Water safety plan ture the attention of policymakers, as they transmit a clear picture easily manual: step-by-step risk management for drinking-water suppliers. Geneva: and accurately. World Health Organization; 2009. fi fi Bennett S, Woods T, Liyanage WM, Smith DL. 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