Environ. Sci. Technol. 2010, 44, 9535–9541
Quantifying Health Improvements from Water Quantity Enhancement: An Engineering Perspective Applied to Rainwater Harvesting in West Africa LAUREN M. FRY,† JOSHUA R. COWDEN,‡ DAVID W. WATKINS, JR.,† THOMAS CLASEN,§ AND J A M E S R . M I H E L C I C * ,⊥ Department of Civil and Environmental Engineering, Michigan Technological University Houghton, Michigan 49931, United States, MWH Global, Fort Collins, Colorado 80525, United States, London School of Hygiene and Tropical Medicine, London, U.K., WC1E 7HT, and Department of Civil and Environmental Engineering University of South Florida, Tampa, Florida, 33620, United States
Received March 11, 2010. Revised manuscript received November 1, 2010. Accepted November 3, 2010.
Knowledge of potential benefits resulting from technological interventions informs decision making and planning of water, sanitation, and hygiene programs. The public health field has built a body of literature showing health benefits from improvements in water quality. However, the connection between improvements in water quantity and health is not well documented. Understandingtheconnectionbetweentechnologicalinterventions and water use provides insight into this problem. We present a model predicting reductions in diarrhea disease burden when the water demands from hygiene and sanitation improvements are met by domestic rainwater harvesting (DRWH). The model is applied in a case study of 37 West African cities. For all cities, with a total population of over 10 million, we estimate that DRWH with 400 L storage capacity could result in a 9% reduction in disability-affected life years (DALYs). If DRWH is combined with point of use (POU) treatment, this potential impact is nearly doubled, to a 16% reduction in DALYs. Seasonal variability of diarrheal incidence may have a small to moderate effect on the effectiveness of DRWH, depending on the storage volume used. Similar predictions could be made for other interventions that improve water quantity in other locations where disease burden from diarrhea is known.
supplying domestic water needs must be evaluated under varying environmental conditions. For example, recent research evaluated effectiveness of domestic rainwater harvesting (DRWH) technology for providing basic water requirements in West Africa, considering seasonal variability, storage capacity, and roof size (2). However, previous studies relating environmental stressors to success of water supply technologies and management strategies have not incorporated quantitative ties to human health outcomes. Health improvement is a fundamental driver for improving water access, evidenced by MDG targets to reduce child mortality, eradicate hunger, and improve maternal health, all of which are impacted by access to safe and sufficient drinking water. Accordingly, the relationship to public health is important when policy makers and practitioners make decisions regarding technology choice for water provision. Successful policies require integration of engineering, public health, and policy research (Figure 1). Engineering analysis provides understanding of stressors on natural resources and human systems; engineering design provides technological solutions for mitigating these stresses; and public health research determines potential health outcomes resulting from environmental stressors and interventions used to implement public health policies. Previously, we used engineering design principles to estimate the potential water quantity enhancement from DRWH (2). In this analysis, we use results from these previous engineering analyses and draw on public health research to predict the potential health impact from changes water quantity provision. The effectiveness of interventions to remove pathogens and improve health is well documented (3-6), so public health policy decisions regarding water quality improvements such as point of use (POU) treatment can be well informed. In the case of technologies that improve water quantity only, however, most studies assess general improvements in water quantity without consideration of the specific amount of water provided (e.g., those studies considered by Esrey (5, 7) and Fewtrell 6, 8). Therefore, health improvements resulting from incremental improvements in water quantity may be
Introduction Sustainable development requires that technology used to meet the Millennium Development Goals (MDGs) be successful under stressors such as population growth, urbanization, land use change and climate change (1). The effectiveness of technologies and management strategies for * Corresponding author. † Michigan Technological University. ‡ MWH Global. § London School of Hygiene and Tropical Medicine. ⊥ University of South Florida. 10.1021/es100798j
2010 American Chemical Society
Published on Web 11/16/2010
FIGURE 1. Engineering analysis, health analysis, and social analysis combine to improve public health External drivers directly affecting human health include various environmental factors (e.g., weather, water quality), socioeconomics (e.g., income, cultural norms), policy (e.g., development of health services), and technology (e.g., implementation of water and sanitation services). Interventions reducing risks caused by these drivers are represented in the “technological and programmatic design” triangle, which is ideally a result of combined efforts in engineering, public health, and social science. VOL. 44, NO. 24, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
9535
less understood by policy makers. We demonstrate a novel approach incorporating environmental engineering analysis to estimate potential health improvements from incremental gains in household water availability. Our approach incorporates public health considerations into evaluation of water quantity enhancement technologies, considering DRWH in West Africa as an example. An engineering analysis procedure is introduced that provides quantitative estimates of potential health benefits from incremental improvements in water supply by considering how different water and sanitation technologies become feasible when more water is available to a household. Our method demonstrates how unique connections among technological design (e.g., DRWH design and knowledge of water intensity of sanitation technologies), environmental processes (e.g., daily rainfall), and public health (e.g., knowledge of risk related to water and sanitation exposure scenarios) can provide new insight for policy makers by allowing estimation of potential public health impacts from incremental improvements in household water availability. Diseases entirely attributed to unsafe water, sanitation, and hygiene (WASH) include schistosomiasis, trachoma, ascariasis, trichuriaisis, and hookworm (9). Typhoid, hepatitis A, and hepatitis E are also water-related diseases attributable in large part to WASH. Water quantity improvements are thought to have the largest effect on “water-washed” diseases such as trachoma and scabies (10). This analysis focuses on reduction of diarrheal diseases, however, because diarrhea represents the single largest burden of disease attributable to WASH, causing 1.5 million deaths annually (11). Epidemiological evidence links health outcomes to WASH (3, 5-8, 12, 13) (Supporting Information (SI) Table S1), and such work has guided engineering improvements in developing countries. According to the earlier analyses, increased quantities of water alone reduces the risk of diarrhea by 20-25%. The most recent study by Waddington and Snilstveit (3), however, suggests that water supply interventions may be ineffective for reducing childhood diarrhea over the long run due to a lack of project sustainability. However, they report the impact is improved when the new water supply is provided at the point of use (21% reduction), and they also report a significant effect (18% reduction) in one high-quality water supply intervention of 12 months or longer. Although based on a small sample size, their findings indicate the need for improved understanding of how incrementally increasing water availability can impact health. ¨ stu Pru ¨ss-U ¨n et al. (14) have developed exposure scenarios associated with levels of risk of infectious diarrhea, based on the type of water and sanitation infrastructure and the fecaloral pathogen load in the environment. They define infectious diarrhea to include cholera, salmonellosis, shigellosis, amoebiasis, and other bacterial, protozoal and viral intestinal diseases. Typhoid and paratyphoid fevers are also partly included in the estimate of infectious diarrhea. These exposure scenarios were developed to incorporate existing knowledge on exposure-risk relationships and information from the Global Water Supply and Sanitation Assessment 2000 (15). Risk of diarrhea decreases from scenario VI to scenario I as the fecal-oral pathogen load decreases from high to low (Table 1). The World Health Organization (WHO) used these six scenarios in the 2002 Comparative Risk Assessment (16), and continues to use them to determine the disease burden from the WASH environmental risk factor. The scenarios remain the accepted method for estimating disease burden from WASH (e.g., ref 17). To estimate burden of disease, the WHO has assigned relative risk (RR) values (Table 1) to these exposure scenarios based on a review of meta-analyses, multicountry studies, and some intervention studies of “superior design” (18), and these RR values remain the best 9536
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 24, 2010
estimates associated with the exposure scenarios (17). Relative risk is defined as the ratio of diarrhea incidence in a group exposed to a risk factor to incidence in an unexposed group. It is clear that access to a sufficient quantity of water can reduce disease burden. However, the extent to which incremental water quantity improvements affect health is less clear. Some low-cost interventions enhance household water availability without necessarily improving water quality (e.g., pumping from a surface water intake or open, shallow wells). Therefore, an alternate method for estimating potential health gains from interventions improving access to additional water without necessarily improving water quality is needed. The WHO defines basic water access, based on Howard and Bartram’s 2003 review (19), as access to a source providing 20 L/person/day within 1 km of the household (11). Among other things, the Howard and Bartram study reviewed norms for minimum requirements of water, which ranged from 15 to 50 L/person/day (See SI Table S2). As a result of the combined importance of water quantity and accessibility, Howard and Bartram defined water service levels, based on level of health concern, from no access (5 L/person/day) to optimal access (more than 100 L/person/day) (see SI Table S3). Note that these estimates do not consider water quality. The WHO also discusses water access in emergencies in terms of a hierarchy of needs (20) in which basic water requirements at the top of the pyramid are met first, and as more water is available, the needs toward the base of the pyramid can be met (SI Table S4). This pyramid suggests that water use for sanitation becomes a priority when an individual has more than 60 L/day. According to the pyramid, the WHO recommendation of 20 L/person/day for basic access is only sufficient for short-term survival, and only provides water for drinking and cooking purposes. In reality, domestic water use for sanitation depends on the sanitation technology used, and can range from zero to more than 18 L per flush (21-25). Previously (26), we estimated the resulting domestic water use when populations with unmet sanitation needs acquired different sanitation technologies (see SI Table S5). Hutton and Haller (27) used the scenarios from the 2002 Comparative Risk Assessment (28) in their cost-benefit analysis of WASH improvements. They included five intervention scenarios that essentially “moved” populations from one exposure scenario to a better one by providing different combinations of WASH improvements. They then used the RR values from Table 1 to determine the resulting reduction in diarrhea incidence and mortality rates. The objective of the analysis presented here is to apply a new engineering analysis methodology to estimate potential public health improvement from increased water supply under the assumption that additional water allows households to improve their level of sanitation and hygiene. This analysis extends that of Hutton and Haller (27) by including engineering analysis that estimates households’ ability to adopt new sanitation and hygiene interventions based on predicted household water availability. We demonstrate the method by applying it to DRWH in West African cities, which has the potential to significantly enhance household water availability. Conversely, the method could be applied to scenarios where changes in climate and land use reduce water supply and lead to increase in disease. The assumption that additional water is used for improvements in sanitation and hygiene means that the analysis provides an estimate of the upper limit of health improvement. Accordingly, incorporation of public health education and commitment to improving sanitation while improving water access would increase the likelihood that this upper limit could be achieved.
TABLE 1. Relative Risk Estimates for Each of the Six Scenarios Used for The WHO’s Comparative Risk Assessment (16). Adapted from Ref 28 exposure scenario (28)
description of water and sanitation access (28)
RR of diarrheal disease (unitless)
daily per capita water use
I
ideal situation, corresponding to the absence of transmission of diarrheal diseases through poor water, sanitation, and hygiene. This situation does not exist in reality.
1
II
population having access to improved water supply and sanitation services in countries where more than 98% of the population is served by those services; generally corresponds to regulated water supply and full sanitation coverage, with partial treatment of sewage, and is typical in developed countries.
2.5
110 L
IIIa
(c) IV plus continuous piped water supply (b) IV and improved personal hygiene (a) IV plus improved water quality (point of use treatment)
no estimate 3.75 4.5
110 L 70 L 50 L
IV
population having access to improved water supply and improved sanitation in countries where less than 98% of the population is served by water supply and sanitation services, and where water supply is likely not to be routinely controlled.
6.9
50 L
V (a)
population having access to improved sanitation but no improved water supply in countries where less than 98% of the population is served by water supply and sanitation services, and where water supply is likely not to be routinely controlled.
6.9
35 L
V (b)
population having access to improved water supply, but not served with improved sanitation in countries which are not extensively covered by those services, and where water supply is not likely to be routinely controlled (less than 98% coverage).
8.7
20 L
VI
no improved water supply and no basic sanitation in a country where less than 98% of the population is served by those services and where water supply is not routinely controlled
11
10
a Relative risk estimates for populations in Scenarios III(a) and III(b) in ref 28 were relative to Scenario II, so these estimates are obtained by multiplying by relative risk in Scenario II (2.5). Solid evidence is still lacking to provide estimates of risk reduction from piped water (Scenario III(c)) (18).
Materials and Methods Our analysis estimates potential reduction in diarrhea disability adjusted life years (DALYs) per month from enhancements in water supply from DRWH. To accomplish this, we estimated water requirements for the six exposure categories presented in Table 1. Our model then uses if-then logic to “move” populations to better exposure scenarios when the required water demand can be met with a 95% reliability. For example, according to Table 1, if a person is currently in exposure scenario VI (requiring 10 L/day) and 40 L/person/day can be provided by DRWH with 95% reliability, then that person will move into exposure scenario IV or III(a), depending on whether POU is added or not, because both IV and III(a) require 50 L/person/day. The WHO exposure scenarios are used, because relative risk estimates associated with these scenarios remain the accepted method for estimating disease burden from WASH (e.g., ref 17). DALYs are a measure of burden of diseasesone DALY is equal to the loss of one healthy life year due to death or the inability to work because of illness. Explanations of specific quantities of water used in each exposure scenario are available in the SI (Table S6). We assume a population can move into a better exposure scenario if the required water can be provided reliably from DRWH. For example, according to SI Table S6, a person in
scenario VI uses 10 L/day, but would demand an additional 10 L/day to move to scenario Vb, because scenario Vb requires 20 L/day. Therefore, if a demand of 10 L/person/day can be met reliably (95% of days), then the population in exposure scenario VI is moved into exposure scenario Vb. Likewise, if a demand of 25 L/person/day can be met, the population in exposure scenario VI would instead move into exposure scenario Va. Because exposure scenario IV and IIIa are essentially the same, with the addition of POU treatment in scenario IIIa, we run the analysis once under the assumption that POU treatment is available and again under the assumption that POU treatment is unavailable. We acknowledge that the use of waterless sanitation would allow populations to move from one scenario to the next with smaller water requirements. However, traditional urban planners may not consider such sanitation options feasible in urban or peri-urban areas where space is limited and where water is a traditional mechanism for transporting excreta away from populations. The analysis may be different in rural settings where waterless sanitation is considered to be a more feasible option. In this analysis, we consider only urban populations. The analysis starts with estimates of populations in each of the exposure categories. Our analysis was conducted for 37 cities in Benin, Togo, Ghana, Burkina Faso, and Cote VOL. 44, NO. 24, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
9537
FIGURE 2. DALYs resulting from diarrhea in each exposure scenario before domestic rainwater harvesting (DRWH) or point of use (POU) treatment and after the intervention at the station where the biggest impact is possible, measured by DALYs avoided per person (gage 655920 in Tabou, Cote d’Ivoire). Exposure scenarios are described in Table 1. Note that we assume that the diarrhea DALYs before implementation of POU or DRWH are distributed evenly throughout the year (i.e., the bar on the left side of each graph showing DALYs before intervention). d’Ivoire. The distribution of the population among the exposure scenarios in the five countries in 2004 (18) is provided in the SI (Table S7). The WHO provides guidelines for calculating burden of disease at national and local levels (29). The fractional reduction in disease following an intervention is defined as the impact fraction (IF).
IF )
∑ p RR - ∑ p′RR ∑ p RR i
i
i
i
i
(1)
i
In eq 1, pi is the proportion of the population in exposure scenario i prior to the intervention, pi’ is the proportion of the population in the exposure scenario i following the intervention, and RRi is the RR for exposure scenario i (Table 1). In our case, the intervention is an increase in water supply obtained from implementation of DRWH. The initial proportion of the population under each exposure category in each city is assumed equal to the proportion of the population under each exposure category in the city’s country (18). The total DALYs avoided following an intervention (DALYs’) is determined as follows: DALYs′ ) DALYs - (IF × DALYs)
(2)
where DALYs is the original number of DALYs before the intervention. The WHO provides national estimates (2004) of DALY rates (DALYs per 100 000 people) (30). We translate these rates to a number of DALYs affecting the urban population by assuming that the DALY rates are constant throughout the country. As a case study, we apply our methodology to DRWH, which is widely practiced throughout the world and can range from collecting water from roof tops in open containers to permanent collection and storage systems (2). Permanent DRWH systems consist of a roof where rain is collected, a 9538
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 24, 2010
gutter that transports water, and a storage system (31). In many Sub-Saharan African countries, DRWH is promoted because of its relatively low cost, reliability, simplicity, and proximity for household use (2). In this study, populations move from one exposure scenario to another depending on the reliability of DRWH to meet the demands shown in Table 1 with 95% reliability. Daily DRWH yield is calculated as the product of roof area, a runoff coefficient, and daily rainfall (31). Estimates of monthly DRWH reliability, calculated as the fraction of days that a specified water demand can be met, can be obtained using long precipitation records. Because complete or long daily rainfall records are not available for many sub-Saharan Africa regions, a first-order Markov stochastic weather generator was used to produce continuous rainfall sequences for the DRWH calculations (2). The first-order Markov model is a lag-one dependence model that uses maximum likelihood estimates of transition probabilities (dry to wet, or wet to dry) to produce sequences of wet and dry days statistically similar to the observed record. A mixed-exponential distribution was then used to model rainfall amounts of wet days. The analysis was conducted for 37 cities in West Africa where rain gages provide sufficient data to predict reliability of DRWH (2). Figure 4 shows the rain gage locations. We assumed an average household roof area of 10 m2 and conducted the analysis for household water storage capacities of 400 and 1000 L. These storage values were chosen because they are reasonable considering possible size limitations in an urban setting, and they represent a range of commercially available water storage units and the range of locally constructed tanks (e.g., ferrocement storage tanks).
Results and Discussion The first bar in each graph in Figure 2 shows the initial distribution of diarrhea DALYs among the scenarios in Tabou, Cote d’Ivoire, the station where DRWH could have the most
FIGURE 3. DALYs that could be avoided by implementing domestic rainwater harvesting (DRWH) and/or point of use (POU) treatment in 37 West African cities.
FIGURE 4. DALYs that could be avoided by implementing domestic rainwater harvesting (DRWH) alone with 400 L storage in 37 West African cities. The greatest impact could be attained by implementing DRWH where the population is large and there are many potential DALYs avoided per person (large, dark circles). impact on health in terms of DALYs avoided per person. Before the introduction of DRWH or POU treatment, there are 702 DALYs per year from diarrhea in Tabou, under the assumption that disease burden from diarrhea in Tabou is consistent with disease burden in Cote d’Ivoire. Given the city’s population (16 000), this translates to about 0.04 DALYs per person annually. In Tabou, DRWH with a storage container of 400 L combined with POU treatment could result in over 170 DALYs avoided annually (0.01 DALYs avoided per person per year). This is roughly a 25% reduction in disease burden. Figure 3 shows how this distribution changes by month, following DRWH and/or POU treatment. The seasonal nature of precipitation means that improvement from the DRWH intervention would mostly occur between May and December, although this is extended to January if storage is increased to 1,000 L. The effectiveness of DRWH for reducing disease burden varies significantly in the region, with the least impact in Accra, Ghana, where DRWH and POU treatment with a 400 L storage capacity could result in only 0.001 DALYs avoided per person per year. This variation is a result of variable rainfall patterns as well as differences in the initial populations in each exposure scenario. For all the cities, with a total population of over 10 million, we estimate that there are about 420 630 DALYs per year from diarrhea, or about 0.042 DALYs per person. DRWH with 400 L storage capacity could result in over 36 700 DALYs avoided (9% reduction). If DRWH is combined with POU treatment, this impact is nearly doubled, with over 68 500 DALYs avoided (16% reduction) (Figure 3). Because there is a wide variety of POU treatment options with varying costs
and material requirements (e.g., clay pot filters, chlorination, slow sand filters, SODIS), incorporation of an appropriate POU treatment into a water access project such as DRWH should be considered. If the storage capacity is increased to 1000 L, then over 71 100 DALYs can be avoided by DRWH alone and over 97 200 DALYs could be avoided by implementing DRWH with POU treatment. The total number of DALYs that could be avoided by implementing DRWH alone in each city is shown in Figure 4. This predictive analysis works under three important assumptions about diarrhea and the effectiveness of interventions to prevent diarrhea. First, we assumed that the distribution of diarrhea DALYs among the exposure scenarios and the effects of water, sanitation, and hygiene interventions are the same throughout the study region. In reality, causes of diarrhea and the impacts of diarrhea and interventions to prevent it are complicated and depend on environment, socioeconomic status, pathogen loading, and immunity, among other things. A second important assumption in this analysis is that diarrhea does not vary seasonally. Seasonal variability of diarrhea results from changes in temperature, relative humidity, and precipitation. Temperature and relative humidity directly affect pathogen replication rates, and precipitation can impact contamination of drinking water. Although extreme rainfall is associated with outbreaks of diarrhea, the relative contribution of these outbreaks to overall incidence is not clear (32). Some individual diarrheacausing diseases are known to vary seasonally or are related to weather extremes (e.g., rotavirus (33) and cholera (34)), and diarrhea incidence has been shown to change with VOL. 44, NO. 24, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
9539
TABLE 2. Results from a Sensitivity Analysis Investigating Possible Effects of Seasonality of Disease on the Potential Reduction in Disease Burden from Domestic Rainwater Harvesting (DRWH) and Point of Use Treatment (POU)a 400 L storage
no seasonal changes case 1, disease reduction during warm, wet months case 2, disease reduction during cool dry months
1000 L storage
DRWH alone
DRWH and POU
DRWH
DRWH and POU
0.0036
0.0068
0.0071
0.0097
0.0023
0.0055
0.0065
0.0092
0.0044
0.0075
0.0072
0.0098
a
Results are reported as DALYs avoided per person in 37 cities in West Africa.
temperature and precipitation for some specific locations (32); however all-cause diarrhea is difficult to relate to seasonality in a regional analysis because of the importance of other factors such as sanitation and human behaviors (34, 35). It is generally known that diarrhea caused by bacteria is more frequent during wet and warm months, while diarrhea caused by viral agents is more prevalent in cool dry months. Therefore, a sensitivity analysis investigating the importance of this assumption was conducted by running the model under two alternative seasonality scenarios for all 37 cities. A systematic review by Levy et al. (33) showed that for every 1 °C increase in temperature and 10 mm increase in precipitation, rotavirus incidence (which accounts for 39% of all diarrhea) decreases by 10% and 1%, respectively. Accordingly, the first seasonality scenario assumes that 39% of the total DALYs are distributed throughout the year according to the Levy et al. results. In the second scenario, because we know of no systematic review investigating diseases prevalent in cool dry months, we simply assume a disease distribution opposite to the seasonality pattern suggested by Levy et al. The inclusion of seasonality of disease had a small to moderate effect on the potential total DALYs avoided by DRWH and DRWH with POU in all 37 cities, depending on storage capacity, as shown in Table 2. For a storage capacity of 400 L, for example, the DALYs avoided by implementing DRWH with POU were reduced by 24% in the first seasonality scenario and increased by 36% in the second scenario. For a storage capacity of 1000 L, the DALYs avoided by implementing DRWH with POU were reduced by 5% in the first seasonality scenario and increased by 7% in the second scenario. The third assumption is that the increased water supply would be used for improvements in sanitation and hygiene. As a result, we note that this analysis predicts an upper limit of the potential health improvement. In fact, the assumption may not be unreasonable, as some literature shows that hygiene improves with the introduction of household water availability (e.g., ref 36). If water use patterns and priorities are known for a specific location, these priorities could be incorporated into this analysis, and may result in less total water being available for sanitation and hygiene. Our analysis demonstrates the importance of ensuring that new water sources be used to improve the sanitation and hygiene level of beneficiaries. The analysis could be improved by a better understanding of seasonality of diarrheal diseases. Additionally, an improved understanding of the local nature of disease transmission, effects of disease on the local population, and impacts of WASH improvements on these diseases in the local setting would improve this analysis. The analysis presented here 9540
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 44, NO. 24, 2010
provides insight, however, into the potential for health improvement from increased water supply that allows populations to improve their WASH status. This insight is gained through a novel combination of knowledge produced by the field of public health with an engineering analysis methodology. Previous to this analysis, methods for predicting impacts of incrementally increasing household water availability were not available. The method is applicable for evaluating potential health effects of interventions that increase water quantity without necessarily improving water quality, and provides a means of estimating cost-effectiveness of such interventions. This integrative analysis can be extended to evaluate other technologies or management plans that result in improvements in water quantity. Likewise, the analysis could investigate how changes in water supply resulting from land use or climate change (e.g., as we are observing in Bolivia (37)) may impact human health.
Acknowledgments This material is based upon work supported by the National Science Foundation under the Sustainable Futures IGERT project (Grant No. DGE 0333401) and Sustainable Development in Bolivia project (Grant No. OISE 0623558 & 0966410). The United States Environmental Protection Agency (EPA) has also provided support under the Greater Research Opportunities Graduate Program. EPA has not officially endorsed this publication, and the views expressed herein are solely the authors’.
Supporting Information Available Seven tables provide epidemiological evidence and water requirements for health. This material is available free of charge via the Internet at http://pubs.acs.org.
Literature Cited (1) Zimmerman, J. B.; Mihelcic, J. R.; Smith, J. Global stressors on water quality and quantity. Environ. Sci. Technol. 2008, 42 (12), 4247–4254. (2) Cowden, J. R.; Watkins, D. W.; Mihelcic, J. R. Stochastic rainfall modeling in West Africa: Parsimonious approaches for domestic rainwater harvesting assessment. J. Hydrol. 2008, 361, 64–77. (3) Waddington, H.; Snilstveit, B. Effectiveness and sustainability of water, sanitation, and hygiene interventions in combating diarrhoea. J. Dev. Eff. 2009, 1 (3), 295–335. (4) Clasen, T.; Roberts, I.; Rabie, T.; Schmidt, W.; Cairncross, S. Interventions to improve water quality for preventing diarrhoea: Systematic review and meta-analysis. BMJ 2007, DOI: 10.1136/ bmj.39118.489931.BE. (5) Esrey, S. A.; Potash, J. B.; Roberts, L.; Shiff, C. Effects of improved water supply and sanitation on ascariasis, diarrhoea, dracunculiasis, hookworm infection, schistosomiasis, and trachoma. Bull. W. H. O. 1991, 69 (5), 609–621. (6) Fewtrell, L.; Colford, J. M. J. Water, sanitation, and hygiene in developing countries: Interventions and diarrhoeasA review. Water Sci. Technol. 2005, 52 (8), 133–142. (7) Esrey, S. A.; Feachem, R. G.; Hughes, J. M. Interventions for the control of diarrhoeal diseases among young children: improving water supplies and excreta disposal facilities. Bull. W. H. O. 1985, 63 (4), 757–772. (8) Fewtrell, L.; Kaufmann, R. B.; Kay, D.; Enanoria, W.; Haller, L. J. M.; Colford, J. Water, sanitation, and hygiene interventions to reduce diarrhoea in less developed countries: A systematic review and meta-analysis. Lancet Infect. Dis. 2005, 5, 42–52. (9) World Health Organization. World Health Report 2002: Reducing Risks, Promoting Healthy Life; World Health Organization Press: Geneva, 2002. (10) Cairncross, S.; Feachem, R. Environmental Health Engineering in the Tropics: An Introductory Text, 2nd ed.; John Wiley & Sons: New York, 1993. ¨ stu (11) Pru ¨ ss-U ¨ n, A.; Bos, R.; Gore, F.; Bartram, J. Safer Water, Better Health: Costs, Benefits and Sustainability of Interventions to Protect and Promote Health; World Health Organization: Geneva, 2008.
(12) Esrey, S. A.; Habricht, J.-P. Epidemiologic evidence for health benefits from improved water and sanitation in developing countries. Epidemiol. Rev. 1986, 8, 117–128. (13) Esrey, S. A. Water, waste, and well-being: A multicountry study. Am. J. Epidemiol. 1996, 143 (6), 608–623. (14) Pruss, A.; Kay, D.; Fewtrell, L.; Bartram, J. Estimating the burden of disease from water, sanitation, and hygiene at a global level. Environ. Health Perspect. 2002, 110 (5), 537–542. (15) World Health Organization. United Nations Children’s Fund. In Global Water Supply and Sanitation Assessment 2000 Report; World Health Organization, UNICEF and Water Supply and Sanitation Collaborative Council: Geneva, 2000. (16) Ezzati, M.; Lopez, A. D.; Rodgers, A.; Murray, C. J. L. Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors; World Health Organization: Geneva, 2002; Vol. 1,2. (17) World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks; World Health Organization: Geneva, 2009. ¨ stu (18) Fewtrell, L.; Pru ¨ ss-U ¨ n, A.; Bos, R.; Gore, F.; Bartram, J. Water, Sanitation and Hygiene: Quantifying the Health Impact at National and Local Levels in Countries with Incomplete Water Supply and Sanitation Coverage; World Health Organization: Geneva, 2007. (19) Howard, G.; Bartram, J. Domestic Water Quantity, Service Level and Health; World Health Organization: Geneva, 2003. (20) World Health Organization. Minimum water quantity needed for domestic use in emergencies; World Health Organization,: Geneva, 2005. (21) WELL. Briefing Note 19: Looking Up the Pipe and Down the Drain: Positioning Sanitation within Integrated Water Resource Management; WELL: Leicestershire, 2007. (22) Gleick, P. H. Basic water requirements for human activities: meeting basic needs. Water International 1996, 21, 83–92. (23) U.S. Environmental Protection Agency. High-Efficiency Toilets (HETs). http://www.epa.gov/watersense/pubs/het.htm (May 4, 2007). (24) UNEP. Environmentally Sound Technologies in wastewater treatment for the implementation of the UNEP Global Programme of Action (GPA): Guidance On Municipal Wastewater; United Nations Environment Programme: Nairobi, 2002. (25) McGarry, M. G. Low-Cost Alternatives to Sewerage, Water Supply and Sanitation in Developing Countries; Ann Arbor Science: Ann Arbor MI, 1982; pp 143-157. (26) Fry, L. M.; Mihelcic, J. R.; Watkins, D. W. Water and nonwaterrelated challenges of achieving global sanitation coverage. Environ. Sci. Technol. 2008, 42 (12), 4298–4304.
(27) Hutton, G.; Haller, L. Evaluation of the Costs and Benefits of Water and Sanitation Improvements at the Global Level; Water, Sanitation and Health, Protection of the Human Environment, World Health Organization: Geneva, 2004. ¨ stu (28) Pru ¨ ss-U ¨ n, A.; Kay, D.; Fewtrell, L.; Bartram, J. Unsafe Water, Sanitation, and Hygiene. In Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors; Ezzati, M., Lopez, A. D., Rodgers, A., Murray, C. J. L., Eds.; World Health Organization: Geneva, 2002; Vol. 2. ¨ stu (29) Pru ¨ ss-U ¨ n, A.; Mathers, C.; Corvala´n, C.; Woodward, A. Introduction and Methods: Assessing the Ennvironmental Burden of Disease at National and Local Levels; World Health Organization: Geneva, 2003. (30) World Health Organization. Death and DALY estimates for 2004 by cause for WHO Member States. http://www.who.int/ healthinfo/global_burden_disease/estimates_country/en/index. html. (31) Mihelcic, J. R.; Fry, L. M.; Myre, E. A.; Phillips, L. D.; Barkldoll, B. D. Field Guide to Environmental Engineering for Development Workers: Water, Sanitation, and Indoor Air; American Society of Civil Engineers Press: Reston, VA, 2009; p 550. (32) McMichael, A.; Campbell-Lendrum, D.; Kovats, S.; Edwards, S.; Wilkinson, P.; Wilson, T.; Nicholls, R.; Hales, S.; Tanser, F.; Sueur, D. L. et al. Comparative Quantification of Health Risks: Global and Regional Burden of Diseases Attributable to Selected Major Risk Factors; World Health Organization: Geneva, 2004; Vol. 2, Chapter 20: Global Climate Change. (33) Levy, K.; Hubbard, A. E.; Eisenberg, J. N. Seasonality of rotavirus disease in the tropics: a systematic review and meta-analysis. Int. J. Epidemiol. 2009, 38 (6), 1487–1496. (34) Kuhn, K.; Campbell-Lendrum, D.; Haines, A.; Cox, J. Using Climate to Predict Infectious Disease Epidemics; World Health Organization: Geneva, 2005. (35) Chambers, R. Seasonal Dimensions to Rural Poverty; Rowman & Littlefield Publishers, Inc.: Lanham, MD , 1981. (36) Curtis, V.; Kanki, B.; Mertens, T.; Traore, E.; Diallo, I.; Tall, F.; Cousens, S. Potties, pits and pipes: Explaining hygiene behaviour in Burkina Faso. Social Sci. Med. 1995, 41 (3), 383– 393. (37) Fry, L.; Watkins, D.; Mihelcic, J.; Reents, N., Sustainability of gravity-fed water systems in alto beni, bolivia: preparing for change. In World Environmental and Water Resources Congress 2010: Challenges of Change, Providence, RI, 2010.
ES100798J
VOL. 44, NO. 24, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
9541
