Identifying opportunities to improve piped water ...

Journal of Cleaner Production 196 (2018) 1e10

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Identifying opportunities to improve piped water continuity and water system monitoring in Honduras, Nicaragua, and Panama: Evidence from Bayesian networks and regression analysis Ryan Cronk*, Jamie Bartram The Water Institute, The University of North Carolina, Chapel Hill, NC, United States

a r t i c l e i n f o

a b s t r a c t

Article history: Received 6 July 2017 Received in revised form 28 March 2018 Accepted 3 June 2018 Available online 4 June 2018

Many piped water systems in rural areas of Latin America and the Caribbean provide discontinuous service. In response to service delivery challenges, governments developed the Rural Water and Sanitation Information System to monitor water service levels, infrastructure conditions, water committees, and technical assistance providers. Collected data are combined into metrics to represent water service sustainability. There is little analysis of these data to identify service delivery and system improvement opportunities and the sustainability metrics are unvalidated. Multivariable regression and Bayesian networks were used to analyze variables associated with the availability of 24-h water services using data from 5560 community-based piped water systems in Honduras, Nicaragua, and Panama. The regression models were compared to the sustainability metric. In Honduras and Nicaragua, the proportion of systems providing 24-h service spanned 71 percentage points between sub-national regions. Good condition infrastructure and year-round water source availability were associated with the availability of 24-h service. The availability of support for system rehabilitation in Honduras and for preventative maintenance in Nicaragua were associated with the availability of 24-h services. The Bayesian networks predicted that good condition infrastructure and year-round water source availability were more influential on the availability of 24-h service than management variables such as the availability of external technical support and funds to rehabilitate the system. In each country, insufficient household water tariffs were collected for 90% or more of systems to cover infrastructure, operations, and maintenance costs. The r-squared values for the regression models ranged from 0.22 (Nicaragua) to 0.49 (Honduras) as compared to 0.05 (Nicaragua) to 0.03 (Honduras) for the sustainability metric e suggesting that regression models are better at predicting higher service levels. Rural water service operators, technical assistance providers, local and national governments, and external support agencies could make better use of monitoring data by using interdisciplinary systems approaches to identify improvement opportunities to allocate technical and financial resources to systems with low service levels. © 2018 Elsevier Ltd. All rights reserved.

Keywords: Functionality Intermittent water supply (IWS) Safely managed drinking water Systems approach Sustainable development goals (SDGs) Sustainability

1. Introduction Continuous, sufficient, safe drinking water services are important for human health, human rights, well-being, and sustainable development (Bartram and Cairncross, 2010). They are urgently needed in rural areas of low- and middle-income countries (LMICs) of Latin American and the Caribbean (LAC) where water service

* Corresponding author. 148 Rosenau Hall, CB #7431. 135Dauer Drive. Chapel Hill, NC, 27599-7431, United States. E-mail address: [email protected] (R. Cronk). https://doi.org/10.1016/j.jclepro.2018.06.017 0959-6526/© 2018 Elsevier Ltd. All rights reserved.

levels are low (Bain et al., 2014a; b). More than 20 million people in rural areas of LAC (16% of the rural population) do not use an improved drinking water source and nearly 40.5 million people in rural areas of LAC (32% of the rural population of LAC) do not use piped drinking water at home (WHO/UNICEF, 2015). Many piped water systems in LMICs are discontinuous, providing less than 24-h of service per day (Kumpel and Nelson, 2016). Systems providing less than 24-h of service per day are more likely to contain fecal indicator bacteria (Kumpel and Nelson, 2013). An estimated 19% of water sources in LAC contain fecal contamination (Bain et al., 2014a,b). People with discontinuous

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R. Cronk, J. Bartram / Journal of Cleaner Production 196 (2018) 1e10

services are more likely to store water at home (Galaitsi et al., 2016) which is more contaminated than source water (Shields et al., 2015). Inadequate drinking water services are a substantial contributor to global burden of disease (Pruss-Ustun et al., 2014). Piped water discontinuity is associated with disease outbreak, including cholera (Jeandron et al., 2015); and piped water system upgrades to continuous service contributed to a reduction in typhoid (Ercumen et al., 2015). People with discontinuous water services may consume water from unsafe, unimproved sources such as surface water. One modeling study suggested that when people consume water from an unimproved source for a few days per month, health gains from a continuous improved source (such as a piped water supply) are negated (Hunter et al., 2009). Another modeling study estimated that intermittent water supply might be responsible for 109,000 diarrheal disability adjusted life years (DALYs) and more than 1500 deaths worldwide every year (Bivins et al., 2017). In response, local and national government and external support actors supporting rural water services in LAC collaboratively n de Agua y Saneamiento developed the Sistema de Informacio Rural (SIASAR) e the Rural Water and Sanitation Information System e to monitor rural drinking water services. SIASAR was developed to provide reliable and comprehensive water service data for “better and more efficient priority setting, policy creation, project planning, and budget allocation” (Rodríguez and Weiss, 2016). The objectives of SIASAR are to collect and consolidate data on communities, water systems, water committees, and technical assistance providers (Rodríguez and Weiss, 2016). These four domains were selected because, in LAC, most systems in rural areas are managed by community water committees and committees conduct management (tariff collection, financial accounting) and operations (day-to-day operations, maintenance). Many committees are volunteer-based and receive post-construction support (PCS) services from technical assistance providers (Rodríguez and Weiss, 2016). Information on water services collected through SIASAR include continuity (number of hours of service per day) and water quality (fecal indicator bacteria, chemical contamination, and chlorine residual). More information is available at the SIASAR website (SIASAR, 2016b). This information is useful for decision-makers and is important to document progress of LAC countries toward policy goals and targets such as the Sustainable Development Goals (SDGs). Target 1 of SDG 6 calls for “universal and equitable access to safe and affordable drinking water for all” (United Nations General Assembly, 2015). Indicator 1 of target 6.1 is the “proportion of [the] population using safely managed drinking water services” (United Nations General Assembly, 2015). Safely managed drinking water services are defined by the WHO/UNICEF Joint Monitoring Programme for Water Supply and Sanitation (JMP) as services that are available at all times (i.e. 24 h per day), on household premises, and free of fecal and priority chemical contamination (WHO/UNICEF, 2017). SIASAR data are combined into metrics for communities, water systems, water committees, and technical assistance providers which are intended to represent water service sustainability. Water systems are scored as ‘A’ (“optimal” service level) through ‘D’ (“lowest” service level). The sustainability metrics comprise 33 community indicators, 37 system-level indicators, 39 water committee indicators, and 44 technical assistance indicators, respectively (Rodríguez and Weiss, 2016). The SIASAR sustainability metric is one of more than 200 related metrics and tools developed to date (Boulenouar et al., 2013). A potential problem with many of these metrics and tools is that, in many cases, each included variable is weighted equally, suggesting

that all variables contribute equally to water service sustainability. However, rural water service sustainability is a complex systems problem. It is multifactorial, often context dependent, requires consideration of multidisciplinary human- and socio-technical systems, and variables contribute differently to water service sustainability (Amjad et al., 2015). For example, studies from subSaharan Africa suggest that system age (Fisher et al., 2015), system type (Cronk and Bartram, 2017), and tariff collection (Foster, 2013), have greater influence on water system sustainability than others such as the availability of alternative water systems and distance to urban centers (Foster, 2013). No large studies from rural areas of LAC examine variables associated with 24-h water service availability or related service parameters. Available studies are small and there are no multivariable analyses, meaning they could not report the relative influence of different variables on water service levels. For example, a study of 60 water systems in El Salvador examined the influence of circuit rider post-construction support (CRPCS, a model for technical assistance to community water system operators) on piped water system quality and sustainability, and found that communities with CRPCS had safer water, higher tariff payment rates, and higher spending for system repairs (Kayser et al., 2014). A study from the Dominican Republic found high levels of maintenance activities and the availability of savings to be associated with higher water system continuity; however, this study only examined 61 communities and effect sizes could not be reported (Schweitzer and Mihelcic, 2012). There is an opportunity to gain further insight from SIASAR by using monitoring data and interdisciplinary systems analysis approaches to identify service improvement opportunities. There are also opportunities to optimize SIASAR monitoring without adding cost or time burden. Bayesian Networks (BNs), which are graphical, probabilistic models that represent and quantify complex relationships, may reveal opportunities to improve services (Cain, 2001). These are useful for examining associations in complex environmental systems, modeling decision-making scenarios, and for use in evidence-based decision-making (Carriger et al., 2016). However, there is little application of BNs to water systems and services, especially in LMICs (Phan et al., 2016). In the largest study of rural water systems conducted to-date in LAC, SIASAR data from Honduras, Nicaragua, and Panama were analyzed using logistic and linear regression and BN models to explore variables that influence water service continuity. The regression models were compared to the SIASAR sustainability metric to examine goodness-of-fit. 2. Methods 2.1. Data sources Data were obtained from the publicly available, online SIASAR database in November 2016 (SIASAR, 2016a). These cross-sectional data had been collected by the government agency responsible for rural water service provision in each country (i.e. the data collection actors) since 2011. The data collection actors intended data collection to be a census of all rural piped water systems in these countries. SIASAR differs from traditional water point surveys (such as those conducted in countries of sub-Saharan Africa) where a similar monitoring method and survey was used in multiple countries and data are intended to be collected over time. Data quality assurance and quality control (QA/QC) measures varied by country. For example, systems and communities in Honduras were revisited several times to verify data, whereas few data checks were conducted in Nicaragua and Panama (Borja-Vega et al., 2017). Full details of SIASAR data collection methods are


described elsewhere (Requejo-Castro et al., 2017). For each country, the water system, community, and water committee datasets were combined to analyze data at the water system level. Technical assistance provider datasets could not be combined at the water system level because they lacked identification codes for the systems serviced. Water system variables in the dataset comprised: continuity, system age, source type (either surface water or groundwater), supply type (gravity-piped system or electric pump piped system), sufficient water available from the source in the summer (i.e. the dry season) and in the winter (i.e. the wet season), watershed condition, and infrastructure condition (for each of the intake, transmission pipeline, storage, and the distribution network). Continuity was measured as the number of hours of service per day. A binary variable “24-h service” was adopted to represent systems that provide 24-h of service versus those that do not. Infrastructure condition was reported as a rating: good condition, requires maintenance, or requires rehabilitation. The definitions of the ratings were similar to a sanitary inspection, which is a water system assessment used to identify actual and potential sources of contamination (WHO, 2011). Data for source type were only available for Honduras. Microbial and chemical water quality data were collected as part of SIASAR monitoring from a sample of systems. However nonsampled systems were not distinguished from those where samples were taken and indicated the water was contaminated; therefore, these data could not be used in the analysis. Community variables included population served by the system and ethnicity. The ethnicity variable was defined as the most prevalent ethnicity in the community and categories included mestizo and different indigenous ethnicities. Water committee-related variables comprised: the legal status of the committee (not legally established; in process of legalization; or legally established), presence of women as water committee members e an important indicator for empowerment of women in water management (Kevany and Huisingh, 2013), committee procedures and regulations in place, minutes available from the last committee meeting, water committee maintains the watershed, committee held a meeting in the past six months, committee has a bank account (to pay for repairs and services), average monthly household tariff rate (set by the committee), availability of replacement funds for system rehabilitation (i.e. savings), and amount of funds available per household. Operations and maintenance-related variables comprised the availability of external technical support (e.g. PCS), the availability of “corrective” maintenance support (i.e. support to rehabilitate the system, availability of preventive maintenance support, and availability of funds for preventative repairs. 2.2. Data analysis: univariable and multivariable linear regression Data were cleaned and analyzed in Stata IC 13.1. Examples of data cleaning included the removal of water system observations with impossible values (e.g. systems providing more than 24-h of service per day). Where appropriate, variable categories were combined to avoid small cell counts (e.g., ethnicity was categorized as either mestizo or indigenous). Variables with small cell counts where categories could not be meaningfully combined were not included in analyses (e.g., sub-national region was not included in analyses of Panama). Tariff values in the SIASAR dataset were converted from the local currency to United States Dollars (USD) using the average exchange rates from 2015. After conversion, values could be compared to estimates of the cost of provision of safely-managed water services in each country (i.e. on premises, continuous, and safe water

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supply) which were calculated in 2015 USDs (Hutton and Varughese, 2016). Average annual expenditure on infrastructure, maintenance, and operations costs per person living in rural areas in each country were obtained from the dataset supporting the 2015 estimates for costs of water services (Hutton and Varughese, 2016). These values were converted to monthly household costs for comparison with the SIASAR data. Average household size in rural areas in Honduras (Secretaría de Salud - SS/Honduras et al., n de 2013) and Nicaragua (Instituto Nacional de Informacio Desarrollo (INIDE) and Ministerio de Salud (MINSA), 2013) were obtained from the most recent demographic survey available for each country. In Panama, the average household size for the country was used (Contraloría General de la República, 2014). Although some of the systems may not have been designed to provide 24-h service, the dependent variable of 24-h service was used in logistic regression since a piped system that is not under constant pressure at all times is subject to risk of contamination (Kumpel and Nelson, 2016). It was also used to compare with the Bayesian network (BN) models, since continuous variables cannot be used in BNs. Independent variables were included in the model if they represented a control, were identified in the literature as a variable associated with water service availability, or were plausibly associated with water service availability. Linear regression using the dependent variable of continuity (hours of service per day) was conducted so that an r-squared value could be generated to compare with the SIASAR sustainability metric (logistic regression outputs only generate pseudo r-squared values). To assess model validity, regression diagnostics were conducted to examine specification errors, goodness-of-fit, multicollinearity, and influential observations. For all analyses, statistical significance was evaluated with a p-value of 0.05 (95% confidence). The SIASAR sustainability metric for water systems was analyzed using continuity as the outcome variable in the regression model to compare r-squared values where the r-squared value is an indicator of model fit that can be used to compare two models. 2.3. Data analysis: Bayesian networks The cleaned datasets for each country were exported from Stata and developed into graphical Bayesian network (BN) models using Netica 5.18 (Norsys Software Corp., 2014). In Bayesian networks, variables are represented as nodes. Each node comprises states (i.e. category of a variable) and arrows representing associations connect nodes. Predicted probabilities of each state are reported for each node. Cycles and dynamic relationships (feedback loops) cannot be represented. The network comprises ‘uncontrollable’ nodes, management nodes, and objective nodes. Uncontrollable nodes (e.g. sub-national region and community ethnicity) are those that influence the overall model but cannot be changed by an intervention. Management nodes are those that can be modified by an intervention (such as the availability of preventative maintenance or availability of funds for operations and maintenance). Objective nodes are the nodes under study; and they are influenced by uncontrollable and management nodes. In this study, the objective node under study is the availability of 24-h water service. All nodes are causally ordered, where distal nodes are connected to proximate nodes that are connected to the objective node. States of each node can be modified to examine the influence of different states on the objective node. The BN model with all states unmodified is the ‘base-case.’ For all analyses, good BN practice (such as defining model objectives and using model evaluations) was followed including for the development of the model structure, wherein the arrangement and direction of association between nodes was made using expert judgement based on the literature and information from domain-knowledge experts. For more on BN

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good practices, see (Cain, 2001) and (Chen and Pollino, 2012). Sensitivity analyses of each BN were conducted to determine which nodes were most influential on 24-h service. The sensitivity analysis in Netica calculates reductions in Shannon's entropy (Pearl, 2014). Ranking all nodes according to entropy reduction identifies those with the most influence on the objective node. Model evaluations were conducted, including calculation of the receiver operating characteristic (ROC), logarithmic loss, quadratic loss, and spherical payoff. For more details on the model evaluations, see (Marcot et al., 2006) and (Morgan et al., 1992). These evaluations are useful for determining the BN model sensitivity and specificity. To conduct the model evaluations, the datasets for each country were randomly split into a dataset to build the model (80% of the original dataset) and a test dataset (20% of the original dataset). ‘Best case’ and ‘worst case’ scenarios were developed and compared to the ‘base case’ by changing management nodes to their highest (best case) and lowest (worst case) states. To explore the influence of seasonal water source availability, scenarios were developed where the nodes ‘sufficient water available from the source in the summer’ (and in the winter) were set to their best and worst states. Because of the causal structure of the BNs, distal nodes may have less influence on the objective node than proximate nodes. Therefore, scenarios were developed where the condition of the transmission pipeline and distribution network were used as objective nodes and the relationships with other nodes was explored. 3. Results 3.1. Descriptive statistics and regression analysis Data from 2946 water systems in Honduras were analyzed (90% gravity-piped systems, 10% electric-pump piped systems), 2115 systems in Nicaragua (67% gravity-piped systems, 33% electricpump piped systems), and 499 systems in Panama (61% gravitypiped systems, 39% electric-pump piped systems). Tables of descriptive statistics are available in the supplementary materials. On average, systems in Honduras provided 18 h of service per day and continuity varied by sub-national region: Lempira region had the highest (22 h/day) and Valle region had the lowest (6 h/ day). Lempira had the highest proportion of systems providing 24-h service (82%) and Valle had the lowest (11%). On average, systems in Nicaragua provided 16 h of service per day and continuity varied by region: RACCN (North Caribbean Coast Autonomous Region) had the highest (21 h/day) and Managua region had the lowest (10 h/ day). RACCN had the highest proportion of systems providing 24-h service (78%) and Masaya had the lowest (7%). On average, systems in Panama provided 18 h of service per day and continuity varied by n region had the highest (21 h/day) and Comarca region: Colo region had the lowest (12 h/day). Panama region had the Embera highest proportion of systems providing 24-h service (84%) and Comarca Ember a had the lowest (40%). In all three countries, gravity-piped systems were more likely to have 24-h service than electric-pump piped systems. In Honduras, systems using groundwater sources were significantly associated with lower availability of 24-h services as compared to surface water sources (OR:0.7, 95%CI:0.5, 0.8, p < 0.001) (Table 1). Source type was removed from the Nicaragua and Panama regressions and BNs due to missing data. In all three countries, sufficient water available from the source in the summer was associated with availability of 24-h service. Regression models and BNs for Nicaragua and Panama are available in the supplementary materials. Nearly half of systems in Panama (47.5%), 22.9% of systems in Nicaragua, and 4.4% of systems in Honduras had no tariff collection.

For more than 90% of systems in all three countries, insufficient monthly household tariffs were collected to cover infrastructure, operations, and maintenance costs (tariff data table available in the supplementary materials). In Honduras, the amount of funds available (categorized into quintiles) was not associated with the likelihood of 24-h service. This variable was not included in the models of Nicaragua and Panama due to missing data. For comparison with other analysis of water service availability e.g. (Foster, 2013) a separate model was developed in which the variable for amount of funds available was replaced with a binary tariff collection variable (tariff collected or no tariffs collected). The binary tariff collection variable was not associated with 24-h service in Honduras. Some water committee-related variables were significantly associated with availability of 24-h service, where in Panama, systems were more likely to provide 24-h service if there were replacement funds available (OR:2; 95%CI: 1.2, 3.5; p ¼ 0.016). In Honduras, availability of corrective maintenance (i.e. services and skills for system rehabilitation) was associated with the availability of 24-h service (OR:2.1; 95% CI: 1.4, 3.4; p ¼ 0.002) and in Nicaragua, preventative maintenance was associated with the availability of 24-h service (OR: 1.3; 95% CI: 1.1, 1.7; p ¼ 0.047). The Panama multivariable model predicted that water systems with intakes in need of rehabilitation (as compared to those in good condition) were less likely to provide 24-h service (OR: 0.3, 95% CI: 0.2, 0.7), p ¼ 0.0061). In multivariable logistic regression models of Honduras and Nicaragua, systems with a distribution network requiring rehabilitation (as compared to systems with networks in good condition) were significantly less likely to provide 24-h service (Honduras: OR: 0.8; 95% CI: 0.6, 1; p ¼ 0.022; Nicaragua: OR: 0.6, 95% CI: 0.4, 0.9; p ¼ 0.013). In Honduras and Nicaragua, systems serving the largest populations were significantly less likely to provide 24-h service as compared to those serving the smallest (highest population quintile versus the lowest, Honduras OR: 0.3, 95% CI: 0.2e0.4, p < 0.0001; Nicaragua OR: 0.4, 95% CI: 0.3, 0.6, p < 0.001). 3.2. Regression model fit compared to the SIASAR sustainability metric The regression model was compared to the SIASAR sustainability metric for water systems (comparison tables are available in the supplementary materials). In Honduras, the r-squared value for the continuity multivariable regression model was 0.49. In comparison, the regression model for the water system sustainability metric in Honduras predicted that ‘A’ rated systems (‘optimal’ service) had higher continuity than ‘B’ or ‘C’ rated systems (none were rated ‘D’, ‘lowest’ level service) and the r-squared value was 0.03. In Nicaragua, the r-squared value for the continuity model was 0.22 compared to 0.05 for the sustainability metric. In Panama, the r-squared value for the continuity model was 0.33 and 0.08 for the sustainability metric. 3.3. Sensitivity analysis and alternative scenarios using Bayesian networks In model evaluations, the receiver operating characteristic (ROC) in Honduras suggested the model was moderately accurate while Nicaragua and Panama were less accurate (Greiner et al., 2000). In the Honduras BN sensitivity analysis, sufficient water available from the source in the summer (dry season), sufficient water available from the source in the winter (wet season), condition of the storage status, and condition of the distribution network were most influential on the availability of a 24-h service. In Nicaragua, sufficient water available from the source in the summer,


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Table 1 Univariable and multivariable logistic regression model for availability of 24-h water services in Honduras. Explanatory variable

Unadjusted model

Adjusted model

OR

OR

CI

pvalue

CI

(0.4, 0.5)