The Impact of Urban Water Supply Improvements on Infant Enteric Pathogen Infection, Diarrhea, and Growth: Results from the PAASIM Matched Cohort Study

The Impact of Urban Water Supply Improvements on Infant Enteric Pathogen Infection, Diarrhea, and Growth: Results from the PAASIM Matched Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Impact of Urban Water Supply Improvements on Infant Enteric Pathogen Infection, Diarrhea, and Growth: Results from the PAASIM Matched Cohort Study Matthew Freeman, Courtney Victor, Joshua Garn, Rebecca Kann, Christine Fagnant-Sperati, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6697339/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract We conducted a matched-control study in Beira, Mozambique to assess the impact of neighborhood-level urban water system improvements on child health and enteric infections. The intervention did not impact infection with bacteria (aRR 0.97, 95%CI 0.90-1.05) or protozoa (aRR 0.93, 95%CI 0.74-1.16), but did impact overall infection with individual pathogens (aRR 0.90, 95%CI 0.81-0.99) and co-infection prevalence (aRR 0.87, 95%CI 0.78-0.98). We found no association between direct household water connections – independent of intervention status – on prevalence of bacteria (aRR 1.00, 95%CI 0.91, 1.09), protozoa (aRR 0.91, 95%CI 0.72-1.14), or co-infection (aRR 0.98, 95%CI 0.85-1.13). We found no associations with diarrhea, child growth, or child mortality. Our evidence points to potential impacts of the intervention on enteric pathogen infections, but our estimates were imprecise. The impact of the intervention may have been limited by the lack of provision of continuous, reliable water supply, and lack of safe water storage. Health sciences/Diseases/Gastrointestinal diseases/Intestinal diseases/Diarrhoea Health sciences/Diseases/Infectious diseases/Bacterial infection Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Access to water is a human right and fundamental to economic development and growth. Provision of safe drinking water is one of the greatest public health achievements of the 20th century; 1 however, 2.2 billion people – 846 million in urban areas and nearly 50% in sub-Saharan Africa – lack access to a safely managed drinking water source. 2 Poor access to drinking water – alongside lack of access to sanitation and poor hygiene – is responsible for over a million annual deaths due to diarrhea. 3 Beyond diarrhea, repeated enteropathogen infections, regardless of symptoms, can lead to linear growth faltering. 3 In Mozambique, 27% of stunting is attributed to unimproved water and sanitation. 4 Improvements to water systems can improve drinking water quality and increase the water available for hygiene and other health behaviors. Movement up the water access ladder – from unimproved to safely managed supply – is estimated to reduce diarrhea by 37%; however, most studies have focused on improving water quality at the household through either chlorination or filtration. 3 The five studies that have evaluated improvements to water service delivery in urban areas have found mixed impacts on diarrhea. 5 A matched-controlled study in India found that continuous supply to households with access to multiple water treatment facilities had no impact on diarrhea and bloody diarrhea, but did find an impact on Typhoid, 6 compared to households that only relied on water from a single facility with non-continuous supply. A household-matched study in two Yemini villages found an increase, but no statistical difference, in childhood diarrhea in households with piped water and sanitation improvements compared to those using traditional well water and water trucks. 7 An intervention in Morocco to promote household-level water connections found no impact on diarrhea; 8 however, a double-difference analysis of neighborhood-level water connections found a 47% reduction in diarrhea risk among children in neighborhoods who received connections compared to those that did not. 9 Similarly, in Bangladesh, a randomized trial of in-line chlorination systems – with no additional infrastructural improvements – reduced diarrheal disease among children under five by 23%, compared to controls. 10 No trials of water system improvements have been conducted in sub-Saharan Africa and none have focused on children under 1 year of age. An estimated US $ 3.95 billion annually in official development assistance is spent on water, sanitation, and hygiene (WASH) projects in Africa, the majority on improved water supply and sanitation systems. 11 There have been recent calls for more “transformative” improvements to WASH to prevent infectious disease, 12 given the limited impact and sustained uptake of point-of-use interventions. This, coupled with the sectoral move towards professionalization of WASH services, 13 increases the need to quantify the impact of large scale, urban water supply investments. This need is especially salient given that an estimated 2.5 billion people (68% of the global population) are expected to move to urban areas by 2050 – predominantly in Asia and Africa . 14 The limited empirical evidence on the effectiveness of infrastructural urban water supply improvements is a gap for policy-makers wishing to understand the cost-effectiveness of WASH investments. More direct and objective measures are required to inform health and development investments. 15 The evidence for the impact of WASH interventions on child health are primarily based on caregiver reported all-cause diarrhea, 3 which is subject to considerable bias, 16 and is a non-specific outcome. Recent advances in multiplex laboratory techniques facilitate assessment of the impact of WASH interventions on a suite of pathogens. 17 Understanding the impact of WASH on specific enteric pathogens is crucial, as chronic and repeated enteric pathogen infections in the first two years of life—with or without symptomatic diarrhea—are associated with serious morbidities, including gut impairment, growth shortfalls, and cognitive deficits by ages 7–9 years. 18 Shedding of enteropathogens provides a direct indicator of current carriage, and increasingly is being used in the WASH field as a proxy measure of infection. Understanding the impact of an intervention designed to block a given transmission pathway (e.g., water) on specific pathogens can provide additional information about dominant pathogen transmission pathways. Here we report on the impact of an urban water supply infrastructure intervention on rates of enteropathogen infections, as well as other child health outcomes—diarrhea, growth, and mortality—among children at 12-months of age in low-income, informal neighborhoods in Beira, Mozambique. We assess the impact of the intervention in neighborhoods receiving water supply improvements and additionally report on associations between households with and without direct water supply connections. Our theory of change for this study is shown in Fig. 1 . RESULTS Design parameters We recruited 898 pregnant women at the last trimester of pregnancy starting in February 2021 and followed the infant-mother dyads until the child was 12 months old, through November 2023, or until they moved elsewhere (n = 174) or otherwise were lost to follow-up (n = 82; Fig. 2 ). Data from 297 children in the intervention and 303 children in the comparison arms were available for our assessment of the impact of our intervention (“Network Effect”). Data on an additional 30 children that moved during the study were included in our analysis of associations with having a household connection (“Direct Connection Effect”). There was relative balance between arms across key demographic characteristics (Table 1 ), except for fixed employment of the primary wage earner (31.0% intervention vs 41.6% comparison) and those with at least secondary education (25.3% intervention vs 18.5% comparison). Table 1 Characteristics of study participants in the PAASIM Study at the initial enrollment visit or first study visit after birth of the index child. Intervention a (N = 297) Comparison a (N = 303) Direct connection a (N = 257) No direct connection a (N = 373) Household demographics Number of children under 5 in household b 0.8 (0.8) 0.7 (0.8) 0.7 (0.8) 0.7 (0.7) Number of people living in household 5.0 (2.4) 4.8 (2.4) 5.7 (2.8) 4.4 (2.0) Months living in household ^# 79.7 (100.2) 56.9 (72.4) 87.4 (104.3) 56.7 (75.0) High poverty* e 138 (46.1%) 149 (48.5%) 77 (34.6%) 217 (54.1%) Fixed employment of primary wage earner ^# 92 (31.0%) 126 (41.6%) 99 (43.2%) 127 (31.7%) Secondary education of primary caregiver complete* 75 (25.3%) 56 (18.5%) 83 (36.4%) 57 (14.2%) Maternal and child measures Mother age (years) 25.5 (5.6) 26.3 (5.6) 26.5 (5.9) 25.6 (5.3%) Child sex (Female)* d 149 (50.2%)​ 145 (47.9%)​ 105 (45.9%) 202 (50.4%) Child birthweight (g) d 3118.2(459.9) 3120.1 (479.2) 3176.4 (503.1) 3076.3 (445.2%) Child exclusively breastfed (3 months) d 83 (28.0%) 99 (32.7%) 61 (26.6%) 132 (32.9%) Cesarean section birth d 28 (9.5%) 27 (9.0%) 24 (10.6%) 34 (8.5%) Previous births (binary) 237 (79.8%) 245 (80.9%) 174 (76.0%) 329 (82.0%) Attended post-natal care d 282 (95.0) 272 (90.1%) 213 (93.0%) 368 (92.0%) WASH and environmental measures Data collected during rainy season (Dec-Apr) 151 (50.8%) 148 (48.8%) 111 (48.5%) 204 (50.9%) Any flooding in the household or yard in the last month # 90 (30.4%) 102 (34.0%) 57 (25.0%) 144 (36.2%) Household Food Insecurity Access Scale c 9.98 (6.7) 10.1 (7.2) 9.5 (6.7) 10.41 (7.2) Handwashing station (with soap and water) in house or yard g 100 (33.7%) 76 (25.1%) 96 (41.9%) 90 (22.4%) Basic sanitation access* f 111 (37.4%) 107 (35.3%) 117 (51.1%) 109 (27.2%) Observed human feces in or near the household 3 (1.0%) 1 (0.3%) 1 (0.4%) 3 (0.8%) Observed animal feces in or near the household 34 (11.5%) 25 (8.3%) 28 (12.2%) 32 (8.0%) Data are mean (SD) or n (%). *Prespecified co-variates for both direct connection effect and network effects models; other imbalance covariates included in networks effect (^) and direct connection effect ( # ) models. a There were 31 households that moved into a neighborhood with a different intervention arm but remained in the study. These households were excluded from the analysis of the impact of the intervention but remained in the direct connection analysis. b Since this question was asked of pregnant women at the enrollment timepoint, it is reasonable that the mean number of children under five in the household would be less than 1. c The Household Food Insecurity Access Scale is calculated using a standardized questionnaire which includes 9 questions that distinguish food insecurity. Higher scores indicate greater food insecurity. d These questions were asked at the first post-birth household visit (3-months) rather than at enrollment. e High poverty is defined by a score of \(\:\le\:\) 66 using the Mozambican Simple Poverty Scorecard, 19 indicating a 50% chance of being under the 200% poverty cutoff with ~30% chance of being below the 150% poverty cutoff. f Basic sanitation access is defined by having access to improved facilities which are not shared with other households. 2 g Imbalances in handwashing station with soap or dwelling in the house or yard are not controlled for in the direct connection models as this is on the causal pathway between having a household connection and the water quality and access variables. Network Effect: Impact of the intervention The overall prevalence of enteropathogen infection was high among children at 12 months of age, with 84% (505/600) infected with at least one bacterial or protozoal pathogen and 61% (365/600) co-infected with ≥ 2 pathogens (Table 2 ). The prevalence of infection with any bacterial or protozoal pathogen was 81% in the intervention households and 87% in comparison households (aRR 0.97, 95%CI 0.91–1.03). When analyzed separately, we similarly did not see such evidence for prevalence of any bacterial infection (aRR 0.97, 95%CI 0.90–1.05) or any protozoal infection (aRR 0.93, 95%CI 0.74–1.16). The prevalence of co-infection was lower (57%) in intervention households than in comparison households (65%, aRR 0.87, 95%CI 0.78–0.98). Relatedly, the intervention resulted in a lower number of pathogens per child (β -0.20, 95%CI -0.36, -0.04); this effect was driven by a lower number of children in the intervention arm having ≥ 3 multiple infections compared to the control arm (Fig. 4 ). The intervention impacted co-infection among female children (aRR 0.78, 95% CI 0.64–0.94; Supplemental Table 1), but not male children (aRR 0.97, 95% CI 0.82–1.14; Supplemental Table 4 ). No other differences were found in sex-stratified results (Supplemental Table 1). Table 2 Impact of the intervention on primary and additional study outcomes among 12-month-old children enrolled in the PAASIM Study in Beira, Mozambique. Intervention (N = 297) Comparison (N = 303) Adjusted RR (95% CI) Primary outcomes Prevalence of any bacterial or protozoan pathogen # 242 (81%)​ 263 (87%)​ 0.97 (0.91, 1.03) Prevalence of any bacterial infection # 222 (75%)​ 240 (79%)​ 0.97 (0.90, 1.05) Prevalence of any protozoan pathogen 84 (28%)​ 105 (35%)​ 0.93 (0.74, 1.16) Any co-infection (bacterial, protozoan, or viral pathogens) # 169 (57%)​ 196 (65%)​ 0.87 (0.78, 0.98) Additional outcomes Any virus 89 (30%)​ 106 (35%)​ 0.85 (0.67, 1.07) Any helminth 24 (8%)​ 21 (7%)​ - Diarrhea (1-week period prevalence) 113 (37%)​ 118 (38%)​ 0.98 (0.83, 1.16) Stunting 55 (19%) 62 (21%) 1.06 (0.73, 1.54) Underweight 23 (8%) 24 (8%) 1.20 (0.54, 2.67) Individual pathogens Salmonella 4 (1%)​ 5 (2%)​ -​ Campylobacter 66 (22%)​ 92 (30%)​ 0.86 (0.63, 1.19) EAEC 117 (39%)​ 146 (48%)​ 0.77 (0.64, 0.93) DAEC 221 (74%)​ 219 (72%)​ 1.08 (0.98, 1.19) tEPEC 59 (20%)​ 62 (20%)​ 0.98 (0.74, 1.28) aEPEC 98 (33%)​ 119 (39%)​ 0.90 (0.73, 1.11) EHEC 4 (1%)​ 5 (2%)​ -​ ETEC 31 (10%)​ 29 (10%)​ 0.83 (0.53, 1.30) STEC 0 (0%)​ 0 (0%)​ -​ EIEC/Shigella 45 (15%)​ 48 (16%)​ 0.91 (0.65, 1.28) E. coli 0157 9 (3%)​ 11 (4%)​ -​ C. difficile 21 (7%)​ 22 (7%)​ 0.81 (0.43, 1.54) V. cholerae 3 (1%)​ 9 (3%)​ - Astrovirus 18 (6%)​ 14 (5%)​ -​ Rotavirus 16 (5%)​ 26 (9%)​ 0.74 (0.37, 1.48) Adenovirus 10 (3%)​ 17 (6%)​ 0.63 (0.32, 1.24) Norovirus 30 (10%)​ 36 (12%)​ 0.74 (0.46, 1.20) Sapovirus 29 (10%)​ 29 (10%)​ 0.93 (0.63, 1.39) Cyclospora 1 (0%)​ 0 (0%)​ -​ Entamoeba histolytica 0 (0%)​ 0 (0%)​ -​ Giardia 56 (19%)​ 61 (20%)​ 1.08 (0.72, 1.64) Cryptosporidium 34 (11%)​ 57 (19%)​ 0.67 (0.43, 1.03) Ascaris lumbricoides 15 (5%)​ 11 (4%)​ -​ Trichuris trichiura 12 (4%)​ 10 (3%)​ - Ancyclostoma duodenale 0 (0%)​ 0 (0%)​ -​ Necator americanus 0 (0%)​ 0 (0%)​ -​ Pooled effect - - 0.90 (0.81, 0.99)^ median (IQR) median (IQR) Adjusted β (95% CI) Pathogen count 1 (1)​ 2 (1)​ -0.20 (-0.36, -0.04) Length-for-Age Z-score (LAZ) -0.93 (1.40) -1.15 (1.56) 0.01 (-0.24, 0.25) Weight-for-Age Z-score (WAZ) -0.42 (1.45) -0.48 (1.49) -0.01 (-0.25, 0.23) Data are n (%) or mean ( inter-quartile range ). EAEC = enteroaggregative Escherichia coli. DAEC = Diffusely-adherent E. coli. tEPEC = typical enteropathogenic E. coli. aEPEC = atypical enteropathogenic E. coli. EHEC = Enterohemorrhagic E. coli. ETEC = enterotoxigenic E. coli. STEC = Shiga toxin-producing E. coli. EIEC = Enteroinvasive E. coli. # DAEC and EAEC were not included in the definition of any bacterial pathogen. ^When excluding EAEC and DAEC from pooled analysis (aRR 0.88, 95% CI 0.79–0.98). We controlled for SES, access to at least basic sanitation, education level of the primary caregiver, child sex, time lived in household (months), employment status, and matching strata in all models (Table 1 ). Data from children in intervention and comparison households revealed similar levels of any viral (30% vs 35%, aRR 0.85, 0.67–1.07) and any helminthic infection (8% vs 7%, aRR not calculable; Table 2 ; Fig. 5 A). Overall, we found that the intervention resulted in lower levels of individual pathogen infections. Notable findings include the impact of the intervention on EAEC among children in intervention (39%) versus comparison (48%) households (aRR 0.77, 95%CI 0.64–0.93), and the impact on Cryptosporidium among children in intervention (11%) versus comparison (19%) arms (aRR 0.67, 95%CI 0.43–1.03). A pooled estimate of all pathogens revealed a lower pathogen infection in intervention households than in comparison households (aRR 0.90 95%CI 0.81–0.99; Fig. 5 B); a sensitivity analysis excluding EAEC and DAEC found similar results (aRR 0.88 95%CI 0.79–0.98). We did not find evidence that the intervention substantially impacted enteric pathogen community profiles (PERMANOVA p-value 0.26) (Fig. 6 A). Diarrhea was similar between arms (37% vs. 39%; aRR 0.99, 95%CI 0.80–1.22). The intervention did not impact any growth outcomes ( Table 2 ) . Child mortality was not different in intervention (n = 8) versus comparison households (n = 10). Direct connection effect: Association with direct connection status The prevalence of any bacterial or protozoal pathogen was 82% among households with a direct connection compared to 86% in households without (aRR 0.98, 95%CI 0.91–1.04; Table 3 ; Supplemental Fig. 1 ). We similarly found no evidence of an association for the prevalence of any bacterial infection (aRR 1.00, 95%CI 0.91, 1.09) nor protozoal infection (aRR 0.91, 95%CI 0.72–1.14). The prevalence of co-infection was 58% for children in households with a direct connection, and 63% for children in households without a direct connection (aRR 0.98, 95%CI 0.85–1.13). Direct connection status was not associated with the number of pathogen infections (β -0.03, 95%CI -0.22, 0.07). Table 3 Association between direct connection to the water system at the household or compound and primary and secondary study outcomes among 12-month-old children enrolled in the PAASIM Study in Beira, Mozambique. Direct household water connection (N = 257) No direct household water connection (N = 373) Adjusted RR (95% CI) Primary outcomes Prevalence of any bacterial or protozoan pathogen # 211 (82%)​ 320 (86%)​ 0.98 (0.91, 1.04) Prevalence of any bacterial infection # 195 (76%)​ 293 (79%)​ 1.00 (0.91, 1.09) Prevalence of any protozoan pathogen 74 (29%)​ 123 (33%)​ 0.91 (0.72, 1.14) Any co-infection (bacterial, protozoan, or viral pathogens) # 149 (58%)​ 234 (63%)​ 0.98 (0.85, 1.13) Additional outcomes Any virus 67 (26%)​ 135 (36%)​ 0.82 (0.63, 1.06) Any helminth 18 (7%)​ 28 (8%)​ 0.86 (0.49, 1.52) Diarrhea (1-week period prevalence) 97 (37%)​ 147 (39%)​ 0.99 (0.80, 1.22) Stunting 37 (15%) 85 (23%) 0.75 (0.48, 1.16) Underweight 17 (7%) 33 (9%) 0.72 (0.37, 1.38) Individual pathogens Salmonella 2 (1%)​ 10 (3%)​ -​ Campylobacter 62 (24%)​ 99 (27%)​ 1.04 (0.78, 1.38) EAEC 107 (42%)​ 170 (46%)​ 1.03 (0.85, 1.25) DAEC 192 (75%)​ 268 (72%)​ 1.04 (0.95, 1.13) tEPEC 51 (20%)​ 77 (21%)​ 1.07 (0.74, 1.55) aEPEC 95 (37%)​ 136 (36%)​ 1.00 (0.79, 1.27) EHEC 5 (2%)​ 4 (1%)​ -​ ETEC 17 (7%)​ 46 (12%)​ 0.65 (0.38, 1.09) STEC 0 (0%)​ 0 (0%)​ -​ EIEC/Shigella 39 (15%)​ 58 (16%)​ 1.05 (0.66, 1.67) E. coli 0157 9 (4%)​ 11 (3%)​ -​ C. difficile 25 (10%)​ 20 (5%)​ 1.45 (0.90, 2.33) V. cholerae 2 (1%)​ 11 (3%)​ - Astrovirus 12 (5%)​ 21 (6%)​ 0.69 (0.35, 1.35) Rotavirus 16 (6%)​ 29 (8%)​ 0.95 (0.49, 1.84) Adenovirus 8 (3%)​ 19 (5%)​ 0.91 (0.41, 1.99) Norovirus 19 (7%)​ 49 (13%)​ 0.63 (0.40, 0.97) Sapovirus 21 (8%)​ 38 (10%)​ 0.98 (0.60, 1.58) Cyclospora 1 (0%)​ 0 (0%)​ -​ Entamoeba histolytica 0 (0%)​ 0 (0%)​ -​ Giardia 41 (16%)​ 81 (22%)​ 0.77 (0.54, 1.10) Cryptosporidium 38 (15%)​ 57 (15)%​ 1.07 (0.80, 1.43) Ascaris lumbricoides 10 (4%)​ 16 (4%)​ - Trichuris trichiura 8 (3%)​ 15 (4%)​ -​ Ancyclostoma duodenale 0 (0%)​ 0 (0%)​ -​ Necator americanus 0 (0%)​ 0 (0%)​ -​ Pooled effect - - 1.01 (0.94, 1.07)^ Pathogen count 1 (1.0)​ 1 (1.0)​ -0.03 (-0.22, 0.16) Length-for-Age Z-score (LAZ) -0.88 (1.32) -1.14 (1.58) 0.04 (-0.25, 0.34) Weight-for-Age Z-score (WAZ) -0.20 (1.54) -0.64 (1.43) 0.22 (0.00, 0.45) Data are n (%) or mean (inter-quartile range). EAEC = enteroaggregative Escherichia coli. DAEC = Diffusely-adherent E. coli. tEPEC = typical enteropathogenic E. coli. aEPEC = atypical enteropathogenic E. coli. EHEC = Enterohemorrhagic E. coli. ETEC = enterotoxigenic E. coli. STEC = Shiga toxin-producing E. coli. EIEC = Enteroinvasive E. coli. # DAEC and EAEC were not included in the definition of any bacterial pathogen. ^When excluding EAEC and DAEC from pooled analysis (aRR 0.96, 95% CI 0.85–1.07). We controlled for SES, basic sanitation access, secondary education level of the primary caregiver, child sex, time living in household (months), fixed employment status, flooding in the household or yard and matching strata in all models (Table 1 ). Data from children living in households with and without a direct connection revealed similar levels of any viral infection (26% vs 36%, aRR 0.82, 0.63–1.06) and any helminthic infection (7% vs 8%, aRR 0.86, 95%CI 0.49–1.52). We did not find any clear trend across associations between direct connection and individual pathogens, with the exception of norovirus, which was less prevalent in children living in households with a direct connection (7%) versus those without a direct connection (13%) (aRR 0.63, 95%CI 0.40–0.97).We did not find evidence that having a direct connection substantially impacted pathogen community profiles (PERMANOVA p-value 0.31) (Fig. 6 B). Data from children living in households with and without a direct connection revealed similar 1-week period-prevalence of diarrhea (37% vs. 39%, aRR 0.99, 0.80–1.22). Having a direct connection was associated with higher weight-for-age Z-scores (WAZ; β 0.22, 95% CI 0.00-0.45), but not other measures of growth (Table 3 ). Child mortality was 7 deaths among households with a direct connection and 9 deaths among those without; 2 had unknown status. Interaction between study arm and direct connection status We did not find a statistical interaction between study arm and direct connection status for our primary outcomes nor nearly all additional outcomes ( Supplemental Tables 2–3 ), except for EIEC/Shigella prevalence. However, for many outcomes we observed a trend toward a stronger magnitude of effect for the interaction between intervention study arms and direct connection status ( Supplemental Fig. 2 ). DISCUSSION We evaluated the impact of a large-scale urban water supply intervention among low-income neighborhoods in Beira, Mozambique that resulted in significant but modest improvements to water access and quality. 20 We found inconsistent evidence for the impact of the intervention for individual outcomes; however, taken together the results suggest some evidence for modest impact of the water supply improvement intervention. Though we found no evidence that the intervention reduced the combined prevalence of any bacterial or protozoal infection, nearly all measured pathogens had protective point estimates, some of which conferred considerable benefit, although these estimates were imprecise. The pooled estimate across our pre-specified list of pathogens indicates that the intervention may have resulted in reductions in enteric infections. We found evidence that the intervention reduced pathogen co-infections, which appeared to be driven by co-infections of ≥ 3 pathogens. We found no impact of the intervention on our secondary outcomes of diarrhea, mortality, or growth. We found no association between having a direct household connection (versus no direct connection) and our primary outcomes, and no associations with additional outcomes, with the exception of WAZ, which favored having a direct household connection. When we assessed the combined effect of being in the intervention arm and having a direct household connection, there was a trend of increased reduction in pathogen burden, suggesting that the indirect effects of a neighborhood-level intervention conferred benefit even to those without a household connection, potentially through reducing the localized force of infection. This study contributes important data to our understanding of if and how system-wide water system improvements impact early child gastrointestinal health. As a trial assessing the effectiveness of an intervention to improve urban water supply, our results do not question the potential efficacy of improved water or household water access to reduce fecal pathogen exposure and improve child health. Indeed, elsewhere we report that the intervention led to improvements in microbial water quality (though not in stored water in the home), water access, satisfaction with water services, and water consumption rates. 20 Early infection and heavy pathogen burden are perhaps more important drivers of long-term health than maternally-reported child diarrhea period-prevalence. A reduction in co-infections could have important long-term health implications, as early and repeated infection can lead to growth shortfalls and gut dysbiosis. Early infection with Cryptosporidium , one of the pathogens most impacted by the intervention, is a particular challenge for child health. 21 This internal measure of exposure aligns with our findings on external measures of exposure, 20 that Cryptosporidium was detected in the water supply in Beira. Cryptosporidium is a protozoan largely resistant to chlorination, so our findings support the need to improve filtration systems and reduce the storage of water at the household to prevent recontamination. In addition, the association between direct connection and lower norovirus infections is intriguing given that a similar result was found for norovirus in the WASH-Benefits study in Bangladesh, 22 which calls into question the assumption that viruses are less likely waterborne. The findings reported here suggest that the water supply improvements studied were insufficient to confer substantial reductions in enteropathogen infection rates or other anticipated health gains, which are downstream on our theory of change. Our results should be considered within the context of several factors related to the study design, intervention fidelity, and contextual considerations. First, there may not have been enough intervention households connected to the upgraded network to have made a substantial impact on infection prevalence and other study outcomes. While the intervention did lead to a 34% increase in prevalence of household connections, only 46% of intervention households had direct connections (compared to 36% in control) at the 12-month visit. This biases the result toward the null. Second, as found elsewhere, water intermittency may have limited the potential benefit of the interventions. 23 Water was available on average for 13 hours/day, requiring even those with direct connections to store water (99% of study households reported storing water). Water storage is well known to lead to recontamination, 24 suggesting the need for interventions that not only reduce water loss and improve source water quality, but also ensure continuous water supply and/or promote chlorination and safe storage at the household. Evidence of contamination from household water storage underscores the importance of achieving continuous supply. 20 We found no previous studies that measured the impact of water supply improvements on pathogen infection. However, some did assess the impact on diarrhea and found mixed results. Ercumen et al hypothesized that in their trial in India, 6 the non-statistical increase in diarrhea among intervention households was due to water storage practices. Intermittency mitigated the impact of water supply improvements to confer reductions in diarrhea, or even exacerbated exposure, in data from Yemen, 7 and this is supported by findings in rural water supply as well. 24 The need to reduce water storage and recontamination to fully yield the potential gastrointestinal health benefits of water supply interventions is further highlighted by our findings that a direct household connection was not independently associated with our primary endpoints. Third, the high force of infection among our study population means that even the delivery of high-quality drinking water would likely be insufficient to block transmission across other environmental pathways, such as flies, hands, food, and via domestic animals. 12 Our study was not powered for individual pathogens, which could be associated with different transmission pathways. While traditional sample size calculations dictate that the more prevalent an outcome, the more achievable a statistically significant result, this does not hold with many reoccurring infectious diseases that circulate within a study population. A reanalysis of the WASH-benefits studies in Kenya and Bangladesh revealed that lower pathogen baseline levels would add study power, as would increasing intervention coverage and efficacy to close pathways of infection. 25 In data from India, researchers pointed to household water storage and “non-water” routes as drivers of their null results. 6 Fourth, system-wide improvements in Beira likely improved quality and pressure across all neighborhoods where water was delivered, reducing the exposure differences between intervention and comparison sub-neighborhoods, again potentially biasing our results towards the null. Water quality was high throughout the system: 55% of households had source-water free chlorine levels above 0.2 mg/L, the WHO-recommended guidelines, we found overall low levels of fecal indicator bacteria in the water, 20 and detected few pathogens in source water other than Cryptosporidium . Yet water intermittency resulted in high levels of water storage, and thus, contamination of water within the home. These system-level improvements reduce the importance of the drinking water route of transmission relative to other routes of exposure such as through hands, fomites, food, or animals. Aside from the limitations discussed above, there are considerable challenges with conducting an impact evaluation of a neighborhood-level water supply improvement. As evaluators, we had no control over the implementation of the intervention, the target neighborhoods, or the timing, so we had to work closely with the water utility to understand the timing and scope of the improvements. We also had to focus our research question to the area of the city where we could establish a robust counterfactual, with appropriate matching and controlling variables. Because we were not able to randomize sub-neighborhoods to the intervention, there remains some chance of unobserved confounding. It is functionally impossible to randomize a neighborhood-level infrastructure project, yet estimating the impact of these types of community-level interventions is of critical importance. Despite the inherent challenges, we were able to establish robust counterfactuals to investigate the impact of urban water supply infrastructure improvements on multiple child health outcomes. The inclusion of quasi-experimental evidence in the development of global burden of disease estimates has been prioritized by the WHO. Strengths of the study include the unique aspect of the design that allowed for examination of both neighborhood- and household-level effects, and the inclusion of enteric pathogens as internal measures of exposure. CONCLUSIONS There is little doubt of the potential for broad-scale health, economic, and social benefits conferred by water system improvements, but in this study, improvements in child enteropathogen infection and other health outcomes were limited, potentially hindered by incomplete coverage of direct connections to the household or compound, and/or intermittent supply of the water system. For water supply investments to yield greater reductions in enteropathogen infection among young children, they likely need to reduce the need to store water by increasing system reliability and household connections or be coupled with interventions focused on safe storage. Further, it may be that in high burden settings such as Beira, water supply improvements alone may be insufficient to substantially reduce exposure to fecal pathogens. METHODS Our detailed protocol 26 and pre-analysis plan ( https://osf.io/4rkn6/ ) are reported elsewhere. The study protocol, informed consent forms, and data collection tools were approved by the Mozambique National Bio-Ethics Committee for Health (IRB00002657) and Emory University’s Institutional Review Board (IRB00098584). Study design We conducted a prospective matched-control study Pesquisa Sobre o Acesso à Água e a Saúde Infantil em Moçambique - Research on Access to Water and Children's Health in Mozambique (PAASIM) from 2021–2023 in the coastal city of Beira, the second largest city in Mozambique. The primary aim of PAASIM was to assess the impact of large-scale water system improvements on acute and chronic health outcomes in children in low-income urban neighborhoods. 26 We assessed the impacts of living in sub-neighborhoods with the improved water network compared to living in sub-neighborhoods without improvements (“Network Effect”). We also use this observational cohort to assess the association between having a direct household water connection compared to having no direct connection, without regard to intervention status (“Direct Household Connection Effect”). Intervention The World Bank invested US $ 200 million with the Mozambican public institutions FIPAG (the water utility) and AURA, IP (the water regulatory authority) through the Water Service & Institutional Support (WASIS-II) Project, starting in 2016. FIPAG leveraged this and additional funding to carry out improvements, including rehabilitation of water treatment facilities, replacing failing pipe mains, reticulation of water supply to new areas, improving service in areas with poor coverage or low water pressure, promoting water connections, institutional support for local water utilities, and emergency water supply systems repair. The stated goals in Beira included increasing flow rate by 15,000 m 3 /day, adding 25,000 new household connections, and increasing piped water supply to 16 hrs/day. Some improvements (e.g., water treatment facility rehabilitation) impacted the entire city, while others affected only certain neighborhoods. For this evaluation, we focused on the water supply improvements in areas where a valid counterfactual was available that impacted marginalized populations. We worked closely with engineers at FIPAG to identify neighborhoods slated to receive improvements that had potentially comparable neighborhoods which would not be affected by the planned improvements to the water distribution system. Planned improvements in the intervention neighborhoods included new tertiary water networks linking water mains to households, designed to reduce water loss and reduce illegal connections, thereby increasing the water pressure and quality and increasing the system’s capacity for direct connections. Households in both intervention and comparison neighborhoods were able to connect to the FIPAG water system, but in the intervention neighborhoods, FIPAG promoted new connections, with connection costs covered by households. Process indicators We report elsewhere on an extensive set of process measures for the intervention, which showed modest impact of the intervention. 20 Notably the intervention increased water connections by 34% more connections (aRR 1.34, 95% CI 1.05–1.72), an absolute difference of 10 percentage points (46% (139/302) of intervention households connected to the water network compared to 36% (110/309) in households in comparison neighborhoods). The intervention reduced source water E. coli contamination by 33% and stored water E. coli contamination by 14%. Having a direct connection was associated with a 24% decrease in households with source water E. coli contamination compared to households without a direct connection, but there was no difference in stored water E. coli contamination. Households in intervention neighborhoods and households with a direct connection experienced improved water availability, water pressure, and satisfaction with water services, though differences were modest. Neighborhood and household selection With FIPAG, we identified sub-neighborhoods for recruitment and performed a population-based community survey in November-December, 2020 of ~ 1,700 households. A socioeconomic status (SES) score was constructed using the Mozambique 'simple poverty scorecard', 19 and scores aggregated at the sub-neighborhood level were categorized into tertiles. Housing density was assessed using random grid sampling using Google Earth imagery. 27 We group-matched intervention and comparison sub-neighborhoods on SES and density using coarsened exact matching, resulting in 36 intervention (~ 16,800 households) and 26 comparison (~ 9,500 households) sub-neighborhoods. Outcomes The primary outcomes included: prevalence of any bacterial and/or protozoal pathogen infection; each individual pathogen; and any co-infection at age 12 months. Viral pathogens were excluded from the primary analysis because waterborne transmission is unlikely to dominate for these pathogens. 28 , 29 We excluded Enteroaggregative Escherichia coli (EAEC) and Diffuse Adherent E. coli (DAEC) because their etiology as pathogens is unclear. Other primary outcomes (source drinking water quality and gut microbiome composition at 12 months) will be reported elsewhere ( https://osf.io/4rkn6/ ). Additional outcomes include individual pathogens (separately and pooled), any viral infection, pathogen community similarity, diarrhea period-prevalence, child growth, and all-cause mortality. Sample size and power calculations Our sample size of 548 households––allocated 1:1 in matched comparison sub-neighborhoods–was powered based on the prevalence of any non-viral pathogen. Utilizing data from the MapSan trial for children 10–14 months of age, 30 we used a comparison group prevalence of 70% for any non-viral pathogen, and estimated the ability to detect a relative risk of 0.74, alpha = 0.05, power = 80%, and an estimated intraclass correlation coefficient of 0.05. Our target enrollment of 900 mother-child dyads accounted for loss to follow-up. Participant Recruitment We selected the first 12 months of life because it is a critical development window when children are at high risk of acute and chronic effects of enteropathogen infection, 31 and it is a short enough period of time to avoid changes in water access that might occur. We identified eligible pregnant women through our population-based survey, local health center pre-natal records, study staff visits to under-enrolled sub-neighborhoods, and study participant referral. We used sub-neighborhood enrollment targets proportionate to our density estimates to achieve balance between arms. During an initial pre-birth visit, we consented pregnant women who met our study eligibility: 1) 18 years or older, 2) in third trimester of pregnancy, 3) resides in enrolled study cluster, 4) not planning to move within the next 12 months, 5) carrying a singleton birth​. We re-assessed study eligibility at each follow-up visit. Households were considered lost to follow-up if participants withdrew from the study, were unreachable, or changed any eligibility status above (Fig. 2 ). We continued data collection if participants moved within the study area. Data Collection At each post-birth visit (at 3-, 6-, 9- and 12-months) we carried out a structured household survey and measured child length, weight, and head circumference, and calculated length-for-age and weight-for-age Z-scores. Prevalence of stunting and underweight were defined as two standard deviations below median of the reference population. We asked the caregiver to report diarrhea of the index child in the previous week. We recorded if a child died at any point after enrollment of the mother. Data were collected on electronic tablets using Open Data Kit (ODK) Collect. To facilitate communication with the study team, participants received a 150 MZN phone credit at each visit. No financial incentive was offered per Mozambican guidelines for human subjects research. Stool Collection and Laboratory Analysis Child stool was collected at post-birth household visits, as described elsewhere. Briefly, we placed three 1-mL aliquots in 9-mL temperature-stable lysis buffer (DNA/RNA Shield Fecal Collection Tubes, Zymo Research, Irvine, CA). INS staff also performed parasitological analysis and referred participants for deworming if applicable at the 12-month visit. Stool samples were extracted using the QIAamp Virus 96 QIAcube HT Kit (Qiagen, Germantown, MD) with modifications. We added 200 mL ASL lysis buffer, 0.004 µL ZymoBIOMICS Spike-in Control I (High Microbial Load) (Zymo Research), and 10 µL INFORCE 3 (Zoetis Inc, Kalamazoo, MI) to 1 mL samples. Samples were extracted in duplicate; the extracted nucleic acid duplicates were pooled and then divided into single use aliquots. Extracted nucleic acids were analyzed using the TaqMan Array card (TAC, ThermoFisher Scientific, Waltham, MA, USA), which allows quantification by real-time PCR via a 384-well microfluidic card for simultaneous detection of multiple viral, bacterial, and parasitic enteric pathogen targets as well as antimicrobial resistance genes, 17 customized for our targets of interest. Statistical Analysis We used an intention-to-treat analysis for the Network Effect analysis: to compare children living in designated intervention versus comparison sub-neighborhoods, without regard to uptake/use of the intervention (Fig. 3 ) . We used multivariable log-linear binomial and modified Poisson generalized estimating equations (GEE) regression models; due to issues with model convergence, we used an independent correlation structure with robust standard errors to account for clustering and controlled for the matching strata. We additionally controlled for household- and individual-level confounders, including household SES, household sanitation, mother’s education-level, child sex, and others found to be imbalanced in our baseline assessment. We assessed the similarity of enteric pathogen community profiles between arms using PERMANOVA analysis of binomial presence/absence data for individual pathogens and plotted results using nonparametric multidimensional scaling (NMDS), a data reduction technique. For the Direct Connection Effect: to assess the association with having a direct connection at the household, we applied similar models to those described above, controlling for intervention arm. We also assessed the interaction between the household and sub-neighborhood network variables, to examine the joint effect of the intervention and the direct connection. We pooled the risk ratios of individual pathogens using meta-analysis, allowing us to account for trends in associations between the counterfactual comparisons and individual pathogen results. For analyses using measures across multiple time points, we accounted for clustering. We conducted sex-stratified analyses for outcomes. All lab personnel and field enumerators were blinded to the intervention status of the samples and households. Participants cannot be blinded to their household-level water exposure status or cluster-level exposure status, although they may not have been aware of water improvements in their sub-neighborhood. Two analysts blinded to the group assignments cleaned and independently replicated the analysis. Declarations DECLARATION OF INTERESTS None declared DATA SHARING. Deidentified participant data, replication analysis code, and data dictionary will be made available upon publication upon request for re-analysis, and for additional analyses with data sharing agreement via our OSF site: https://osf.io/4rkn6/ Funding: NIAID R01AI130163. NIEHS T32ES012870 References Cutler, D.; Miller, G. The Role of Public Health Improvements in Health Advances: The Twentieth-Century United States. Demography 2005, 42 (1), 1–22. Data | JMP . https://washdata.org/data (accessed 2024-10-17). Wolf, J.; Johnston, R. B.; Ambelu, A.; Arnold, B. F.; Bain, R.; Brauer, M.; Brown, J.; Caruso, B. A.; Clasen, T.; Colford, J. M.; Mills, J. E.; Evans, B.; Freeman, M. C.; Gordon, B.; Kang, G.; Lanata, C. F.; Medlicott, K. O.; Prüss-Ustün, A.; Troeger, C.; Boisson, S.; Cumming, O. Burden of Disease Attributable to Unsafe Drinking Water, Sanitation, and Hygiene in Domestic Settings: A Global Analysis for Selected Adverse Health Outcomes. 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Supplementary Files PAASIMAuthorshipGroupPrimaryimpactspaper.docx PAASIM Authorship Group SupplementalTable1.docx Supplemental Table 1 SupplementalTable2.docx Supplemental Table 2 SupplementalTable3.docx Supplemental Table 3 SupplementaryFigure1.pdf Supplementary Figure 1: Direct connection effect on enteropathogens SupplementaryFigure2.pdf Supplementary Figure 2: Interaction between direct and network effects on eteropathogens Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6697339","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":485897819,"identity":"ac79d0e1-6920-4c15-afa9-74381dbb5ca2","order_by":0,"name":"Matthew Freeman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYJACZhDB3gDhyMFEGRtwa2BsBlE8ByBcY9K1JMJU4tSi237++OOCisMMPNKHDz66UXMnvV/s8LHPPAw2shsOYNdidiaZsXnGGaAWvrRk45xjz3Jnzk5Lns3DkGaMU8sBoBbettsM9jw8ZtI5bIdzN9zOMWbmYTiciFPL+cdALf9uM/Dw8H//nfPvcLo9RMt/3FpugGxpAGnhYWPObTucYCAN1nIAj5bHhrN5jv0H6TCWzu07bDjjdloy4xyDZOOZOB2W+OAzT02aHA8P88PPOd8Oy/PPTj7M8KbCTrYPhxYY4EHhMfEY4FeOCRh/kKpjFIyCUTAKhjMAAFlIXNg2eT8HAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-1517-2572","institution":"Emory University","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Freeman","suffix":""},{"id":485897820,"identity":"f062e1f1-73b5-4597-85e0-326cc0233c3f","order_by":1,"name":"Courtney Victor","email":"","orcid":"https://orcid.org/0000-0002-3082-6423","institution":"Emory 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Washington","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Fagnant-Sperati","suffix":""},{"id":485897824,"identity":"cb0799ec-e0dd-4df2-9eb6-9d9ba7f9846c","order_by":5,"name":"Erin Kowalsky","email":"","orcid":"","institution":"University of North Carolina - Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Erin","middleName":"","lastName":"Kowalsky","suffix":""},{"id":485897825,"identity":"679c3b3b-07a0-4a4a-82b6-b1a02e1b987a","order_by":6,"name":"João Manuel","email":"","orcid":"","institution":"Instituto Nacional de Saúde","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"","lastName":"Manuel","suffix":""},{"id":485897826,"identity":"4fddabbe-c9fc-4275-89dd-408a6a2b4432","order_by":7,"name":"Magalhães Mangamela","email":"","orcid":"","institution":"Autoridade Reguladora de Água e Saneamento (AURA)","correspondingAuthor":false,"prefix":"","firstName":"Magalhães","middleName":"","lastName":"Mangamela","suffix":""},{"id":485897827,"identity":"eb3ee5a2-2234-4367-9aad-db979fa93666","order_by":8,"name":"Sandy McGunegill","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Sandy","middleName":"","lastName":"McGunegill","suffix":""},{"id":485897828,"identity":"093bfc34-1933-479a-a7bd-8ea9cc6bdab6","order_by":9,"name":"Molly Miller-Petrie","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Molly","middleName":"","lastName":"Miller-Petrie","suffix":""},{"id":485897829,"identity":"fabf9a12-6066-4ec0-b0c7-389352900498","order_by":10,"name":"Jedidiah Snyder","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Jedidiah","middleName":"","lastName":"Snyder","suffix":""},{"id":485897830,"identity":"2d634faa-fb23-4167-b019-52ca0c6e5ee3","order_by":11,"name":"Thomas Clasen","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Clasen","suffix":""},{"id":485897831,"identity":"722a027b-7857-424b-b6bb-761a600350c0","order_by":12,"name":"Konstantinos Konstantinidis","email":"","orcid":"","institution":"Georgia Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Konstantinos","middleName":"","lastName":"Konstantinidis","suffix":""},{"id":485897832,"identity":"56c23d77-6527-4d01-9410-c4e998b8f0ed","order_by":13,"name":"Joe Brown","email":"","orcid":"","institution":"UNC Chapel Hill","correspondingAuthor":false,"prefix":"","firstName":"Joe","middleName":"","lastName":"Brown","suffix":""},{"id":485897833,"identity":"31a38391-9c55-4475-ad17-bcb8b34e54d8","order_by":14,"name":"Rassul Nalá","email":"","orcid":"","institution":"Instituto Nacional de Saúde","correspondingAuthor":false,"prefix":"","firstName":"Rassul","middleName":"","lastName":"Nalá","suffix":""},{"id":485897834,"identity":"c0297009-50b9-4a19-8a29-3c2cd7e13ce1","order_by":15,"name":"Karen Levy","email":"","orcid":"","institution":"University of Washington","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Levy","suffix":""}],"badges":[],"createdAt":"2025-05-19 09:32:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6697339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6697339/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87051209,"identity":"d8aec06e-3c0f-4e14-b7b4-2113817a3415","added_by":"auto","created_at":"2025-07-18 15:02:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45799,"visible":true,"origin":"","legend":"\u003cp\u003eTheory of Change for the PAASIM Study in Beira, Mozambique\u003cstrong\u003e \u003c/strong\u003ewhereby the intervention improves drinking water access and quality, leading to a reduction in pathogen exposure, affecting gastrointestinal conditions (e.g., pathogen infections), and ultimately improving health (e.g., diarrhea, growth).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/5f994815baf9836e4a0bf551.png"},{"id":87053924,"identity":"98ed0797-a8ff-425b-a66a-aa4695e50c14","added_by":"auto","created_at":"2025-07-18 15:18:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":179684,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Flow Diagram for the PAASIM Study in Beira, Mozambique\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/0e44ca712f06394c7cefccd3.png"},{"id":87051217,"identity":"c5a537a5-799e-444c-8521-69e2b19269f6","added_by":"auto","created_at":"2025-07-18 15:02:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82047,"visible":true,"origin":"","legend":"\u003cp\u003eTypes of Household Comparisons from the PAASIM Study in Beira, Mozambique\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/44dfee141e2244ec51d9f234.png"},{"id":87051212,"identity":"932c61a4-00c2-469b-b8fc-417a50e36188","added_by":"auto","created_at":"2025-07-18 15:02:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":35639,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of children with co-infections by intervention arm in the PAASIM Study in Beira, Mozambique\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/55ee90fcbe63236b249c548f.png"},{"id":87053179,"identity":"7e74ed01-2142-4133-afab-575227b8f003","added_by":"auto","created_at":"2025-07-18 15:10:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":78075,"visible":true,"origin":"","legend":"\u003cp\u003eAdjusted impact of the intervention on (A) pathogen taxa and coinfection, growth, and diarrhea and (B) individual pathogen measures, including the pooled analysis, among 12-month-old children enrolled in the PAASIM Study in Beira, Mozambique.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/209ac5de33bd8bd9d3c2db25.png"},{"id":87053176,"identity":"fde25082-56bc-49fc-ba30-ed7c5419bf6b","added_by":"auto","created_at":"2025-07-18 15:10:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eUnadjusted non-metric multidimensional scaling (NMDS) plot for pathogen community dissimilarity by A) intervention and B) direct connection to the water system at the household or compound among 12-month-old children enrolled in the PAASIM Study in Beira, Mozambique. Adjusted PERMANOVA \u003cem\u003ep\u003c/em\u003e-value is presented in the text.\u003c/p\u003e","description":"","filename":"placeholderimage.png","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/a2d88b7b008db39ce1cfb965.png"},{"id":100367517,"identity":"b03539cb-f248-45be-8024-8da9c9c1b226","added_by":"auto","created_at":"2026-01-16 07:57:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1826162,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/b5f45259-4c33-4dea-8e38-1dcc734cf780.pdf"},{"id":87051210,"identity":"c2446de3-d373-4549-8c79-766c7a618c4a","added_by":"auto","created_at":"2025-07-18 15:02:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16014,"visible":true,"origin":"","legend":"PAASIM Authorship Group","description":"","filename":"PAASIMAuthorshipGroupPrimaryimpactspaper.docx","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/ad781ceaf4abdf421d10d540.docx"},{"id":87051211,"identity":"aa661a38-0e9d-40d4-9423-ea5beb8cc921","added_by":"auto","created_at":"2025-07-18 15:02:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15317,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Table 1\u003c/p\u003e","description":"","filename":"SupplementalTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/eaf9cbd48fe8e494e522edfc.docx"},{"id":87051213,"identity":"d9ab2dea-906a-4335-a085-da7820193bec","added_by":"auto","created_at":"2025-07-18 15:02:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18549,"visible":true,"origin":"","legend":"Supplemental Table 2","description":"","filename":"SupplementalTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/b14e2520e99b087175f7a438.docx"},{"id":87051218,"identity":"02bea1c4-024d-49ff-95cf-40d50dcf711b","added_by":"auto","created_at":"2025-07-18 15:02:36","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":26509,"visible":true,"origin":"","legend":"Supplemental Table 3","description":"","filename":"SupplementalTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/790758430c8515ab9de7f6d3.docx"},{"id":87051219,"identity":"1efb7102-123c-4939-82d8-838cfa3f5724","added_by":"auto","created_at":"2025-07-18 15:02:36","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":345947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1: \u003c/strong\u003eDirect connection effect on enteropathogens\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/5582563700ad1007fd38aebc.pdf"},{"id":87053182,"identity":"644e10d4-0848-4b37-b1f7-311f1c30bdff","added_by":"auto","created_at":"2025-07-18 15:10:37","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":695810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2: \u003c/strong\u003eInteraction between direct and network effects on eteropathogens\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6697339/v1/3322b4d61f98a3ef4c800bf8.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"The Impact of Urban Water Supply Improvements on Infant Enteric Pathogen Infection, Diarrhea, and Growth: Results from the PAASIM Matched Cohort Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAccess to water is a human right and fundamental to economic development and growth. Provision of safe drinking water is one of the greatest public health achievements of the 20th century;\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e however, 2.2\u0026nbsp;billion people \u0026ndash; 846\u0026nbsp;million in urban areas and nearly 50% in sub-Saharan Africa \u0026ndash; lack access to a safely managed drinking water source.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Poor access to drinking water \u0026ndash; alongside lack of access to sanitation and poor hygiene \u0026ndash; is responsible for over a million annual deaths due to diarrhea.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Beyond diarrhea, repeated enteropathogen infections, regardless of symptoms, can lead to linear growth faltering.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e In Mozambique, 27% of stunting is attributed to unimproved water and sanitation.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eImprovements to water systems can improve drinking water quality and increase the water available for hygiene and other health behaviors. Movement up the water access ladder \u0026ndash; from unimproved to safely managed supply \u0026ndash; is estimated to reduce diarrhea by 37%; however, most studies have focused on improving water quality at the household through either chlorination or filtration.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The five studies that have evaluated improvements to water service delivery in urban areas have found mixed impacts on diarrhea.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e A matched-controlled study in India found that continuous supply to households with access to multiple water treatment facilities had no impact on diarrhea and bloody diarrhea, but did find an impact on Typhoid,\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e compared to households that only relied on water from a single facility with non-continuous supply. A household-matched study in two Yemini villages found an increase, but no statistical difference, in childhood diarrhea in households with piped water and sanitation improvements compared to those using traditional well water and water trucks.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e An intervention in Morocco to promote household-level water connections found no impact on diarrhea;\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e however, a double-difference analysis of neighborhood-level water connections found a 47% reduction in diarrhea risk among children in neighborhoods who received connections compared to those that did not.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Similarly, in Bangladesh, a randomized trial of in-line chlorination systems \u0026ndash; with no additional infrastructural improvements \u0026ndash; reduced diarrheal disease among children under five by 23%, compared to controls.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e No trials of water system improvements have been conducted in sub-Saharan Africa and none have focused on children under 1 year of age.\u003c/p\u003e\u003cp\u003eAn estimated US\u003cspan\u003e$\u003c/span\u003e3.95\u0026nbsp;billion annually in official development assistance is spent on water, sanitation, and hygiene (WASH) projects in Africa, the majority on improved water supply and sanitation systems.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e There have been recent calls for more \u0026ldquo;transformative\u0026rdquo; improvements to WASH to prevent infectious disease,\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e given the limited impact and sustained uptake of point-of-use interventions. This, coupled with the sectoral move towards professionalization of WASH services,\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e increases the need to quantify the impact of large scale, urban water supply investments. This need is especially salient given that an estimated 2.5\u0026nbsp;billion people (68% of the global population) are expected to move to urban areas by 2050 \u0026ndash; predominantly in Asia and Africa .\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e The limited empirical evidence on the effectiveness of infrastructural urban water supply improvements is a gap for policy-makers wishing to understand the cost-effectiveness of WASH investments.\u003c/p\u003e\u003cp\u003eMore direct and objective measures are required to inform health and development investments.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e The evidence for the impact of WASH interventions on child health are primarily based on caregiver reported all-cause diarrhea,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e which is subject to considerable bias,\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and is a non-specific outcome. Recent advances in multiplex laboratory techniques facilitate assessment of the impact of WASH interventions on a suite of pathogens.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eUnderstanding the impact of WASH on specific enteric pathogens is crucial, as chronic and repeated enteric pathogen infections in the first two years of life\u0026mdash;with or without symptomatic diarrhea\u0026mdash;are associated with serious morbidities, including gut impairment, growth shortfalls, and cognitive deficits by ages 7\u0026ndash;9 years.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Shedding of enteropathogens provides a direct indicator of current carriage, and increasingly is being used in the WASH field as a proxy measure of infection. Understanding the impact of an intervention designed to block a given transmission pathway (e.g., water) on specific pathogens can provide additional information about dominant pathogen transmission pathways.\u003c/p\u003e\u003cp\u003eHere we report on the impact of an urban water supply infrastructure intervention on rates of enteropathogen infections, as well as other child health outcomes\u0026mdash;diarrhea, growth, and mortality\u0026mdash;among children at 12-months of age in low-income, informal neighborhoods in Beira, Mozambique. We assess the impact of the intervention in neighborhoods receiving water supply improvements and additionally report on associations between households with and without direct water supply connections. Our theory of change for this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDesign parameters\u003c/h2\u003e\u003cp\u003eWe recruited 898 pregnant women at the last trimester of pregnancy starting in February 2021 and followed the infant-mother dyads until the child was 12 months old, through November 2023, or until they moved elsewhere (n\u0026thinsp;=\u0026thinsp;174) or otherwise were lost to follow-up (n\u0026thinsp;=\u0026thinsp;82; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Data from 297 children in the intervention and 303 children in the comparison arms were available for our assessment of the impact of our intervention (\u0026ldquo;Network Effect\u0026rdquo;). Data on an additional 30 children that moved during the study were included in our analysis of associations with having a household connection (\u0026ldquo;Direct Connection Effect\u0026rdquo;). There was relative balance between arms across key demographic characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), except for fixed employment of the primary wage earner (31.0% intervention vs 41.6% comparison) and those with at least secondary education (25.3% intervention vs 18.5% comparison).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of study participants in the PAASIM Study at the initial enrollment visit or first study visit after birth of the index child.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntervention\u003csup\u003ea\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;297)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComparison\u003csup\u003ea\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;303)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDirect connection\u003csup\u003ea\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo direct connection\u003csup\u003ea\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;373)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold demographics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of children under 5 in household\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.8 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.7 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.7 (0.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of people living in household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.0 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.8 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.7 (2.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.4 (2.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMonths living in household\u003csup\u003e^#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.7 (100.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.9 (72.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.4 (104.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e56.7 (75.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh poverty*\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e138 (46.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e149 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77 (34.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e217 (54.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed employment of primary wage earner\u003csup\u003e^#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92 (31.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e126 (41.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e99 (43.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e127 (31.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSecondary education of primary caregiver complete*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56 (18.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57 (14.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal and child measures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMother age (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.5 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.3 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.5 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.6 (5.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild sex (Female)*\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e149 (50.2%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e145 (47.9%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105 (45.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202 (50.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild birthweight (g)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3118.2(459.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3120.1 (479.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3176.4 (503.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3076.3 (445.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild exclusively breastfed (3 months)\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83 (28.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99 (32.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61 (26.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e132 (32.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarean section birth\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24 (10.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e34 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevious births (binary)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e237 (79.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e245 (80.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e174 (76.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e329 (82.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttended post-natal care\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e282 (95.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e272 (90.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e213 (93.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e368 (92.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWASH and environmental measures\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData collected during rainy season (Dec-Apr)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e151 (50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148 (48.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e111 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e204 (50.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny flooding in the household or yard in the last month\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102 (34.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e57 (25.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e144 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold Food Insecurity Access Scale\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.98 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.1 (7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.5 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.41 (7.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHandwashing station (with soap and water) in house or yard\u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100 (33.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76 (25.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96 (41.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e90 (22.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic sanitation access*\u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111 (37.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107 (35.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e117 (51.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e109 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObserved human feces in or near the household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (1.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1 (0.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3 (0.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObserved animal feces in or near the household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25 (8.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eData are mean (SD) or n (%). *Prespecified co-variates for both direct connection effect and network effects models; other imbalance covariates included in networks effect (^) and direct connection effect (\u003csup\u003e#\u003c/sup\u003e) models.\u003c/p\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eThere were 31 households that moved into a neighborhood with a different intervention arm but remained in the study. These households were excluded from the analysis of the impact of the intervention but remained in the direct connection analysis.\u003c/p\u003e\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eSince this question was asked of pregnant women at the enrollment timepoint, it is reasonable that the mean number of children under five in the household would be less than 1. \u003csup\u003ec\u003c/sup\u003eThe Household Food Insecurity Access Scale is calculated using a standardized questionnaire which includes 9 questions that distinguish food insecurity. Higher scores indicate greater food insecurity. \u003csup\u003ed\u003c/sup\u003eThese questions were asked at the first post-birth household visit (3-months) rather than at enrollment. \u003csup\u003ee\u003c/sup\u003eHigh poverty is defined by a score of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\le\\:\\)\u003c/span\u003e\u003c/span\u003e66 using the Mozambican Simple Poverty Scorecard,\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e indicating a 50% chance of being under the 200% poverty cutoff with ~30% chance of being below the 150% poverty cutoff. \u003csup\u003ef\u003c/sup\u003eBasic sanitation access is defined by having access to improved facilities which are not shared with other households.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e g\u003c/sup\u003eImbalances in handwashing station with soap or dwelling in the house or yard are not controlled for in the direct connection models as this is on the causal pathway between having a household connection and the water quality and access variables.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNetwork Effect: Impact of the intervention\u003c/h3\u003e\n\u003cp\u003eThe overall prevalence of enteropathogen infection was high among children at 12 months of age, with 84% (505/600) infected with at least one bacterial or protozoal pathogen and 61% (365/600) co-infected with \u0026ge;\u0026thinsp;2 pathogens (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The prevalence of infection with any bacterial or protozoal pathogen was 81% in the intervention households and 87% in comparison households (aRR 0.97, 95%CI 0.91\u0026ndash;1.03). When analyzed separately, we similarly did not see such evidence for prevalence of any bacterial infection (aRR 0.97, 95%CI 0.90\u0026ndash;1.05) or any protozoal infection (aRR 0.93, 95%CI 0.74\u0026ndash;1.16). The prevalence of co-infection was lower (57%) in intervention households than in comparison households (65%, aRR 0.87, 95%CI 0.78\u0026ndash;0.98). Relatedly, the intervention resulted in a lower number of pathogens per child (β -0.20, 95%CI -0.36, -0.04); this effect was driven by a lower number of children in the intervention arm having\u0026thinsp;\u0026ge;\u0026thinsp;3 multiple infections compared to the control arm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The intervention impacted co-infection among female children (aRR 0.78, 95% CI 0.64\u0026ndash;0.94; Supplemental Table\u0026nbsp;1), but not male children (aRR 0.97, 95% CI 0.82\u0026ndash;1.14; \u003cb\u003eSupplemental Table\u0026nbsp;4\u003c/b\u003e). No other differences were found in sex-stratified results \u003cb\u003e(Supplemental Table\u0026nbsp;1).\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eImpact of the intervention on primary and additional study outcomes among 12-month-old children enrolled in the PAASIM Study in Beira, Mozambique.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;297)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComparison\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;303)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdjusted RR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary outcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence of any bacterial or protozoan pathogen\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e242 (81%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e263 (87%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97 (0.91, 1.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence of any bacterial infection\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e222 (75%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240 (79%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.97 (0.90, 1.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence of any protozoan pathogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84 (28%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e105 (35%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93 (0.74, 1.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny co-infection (bacterial, protozoan, or viral pathogens) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169 (57%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e196 (65%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.87 (0.78, 0.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdditional outcomes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny virus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (30%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (35%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.85 (0.67, 1.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny helminth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (8%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (7%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiarrhea (1-week period prevalence)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113 (37%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (38%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98 (0.83, 1.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStunting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.06 (0.73, 1.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.54, 2.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndividual pathogens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCampylobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (22%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e92 (30%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86 (0.63, 1.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEAEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e117 (39%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e146 (48%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77 (0.64, 0.93)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDAEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e221 (74%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219 (72%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08 (0.98, 1.19)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etEPEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (20%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (20%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98 (0.74, 1.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaEPEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98 (33%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (39%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.73, 1.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEHEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4 (1%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (10%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (10%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83 (0.53, 1.30)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIEC/Shigella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (15%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48 (16%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.91 (0.65, 1.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eE. coli 0157\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (4%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC. difficile\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (7%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (7%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.81 (0.43, 1.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eV. cholerae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3 (1%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAstrovirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (6%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (5%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRotavirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (5%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (9%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74 (0.37, 1.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdenovirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (6%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63 (0.32, 1.24)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorovirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (10%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (12%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74 (0.46, 1.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSapovirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (10%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (10%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.93 (0.63, 1.39)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyclospora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEntamoeba histolytica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGiardia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (19%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61 (20%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08 (0.72, 1.64)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCryptosporidium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (11%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (19%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67 (0.43, 1.03)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAscaris lumbricoides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (5%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (4%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTrichuris trichiura\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (4%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAncyclostoma duodenale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNecator americanus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePooled effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90 (0.81, 0.99)^\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003emedian (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003emedian (IQR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eAdjusted β\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(95% CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathogen count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (1)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.20 (-0.36, -0.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength-for-Age Z-score (LAZ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.93 (1.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.15 (1.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01 (-0.24, 0.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight-for-Age Z-score (WAZ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.42 (1.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.48 (1.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01 (-0.25, 0.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eData are n (%) or mean (\u003cem\u003einter-quartile range\u003c/em\u003e). \u003cem\u003eEAEC\u0026thinsp;=\u0026thinsp;enteroaggregative\u003c/em\u003e Escherichia coli. \u003cem\u003eDAEC\u0026thinsp;=\u0026thinsp;Diffusely-adherent\u003c/em\u003e E. coli. \u003cem\u003etEPEC\u0026thinsp;=\u0026thinsp;typical enteropathogenic\u003c/em\u003e E. coli. \u003cem\u003eaEPEC\u0026thinsp;=\u0026thinsp;atypical enteropathogenic\u003c/em\u003e E. coli. EHEC\u0026thinsp;=\u0026thinsp;\u003cem\u003eEnterohemorrhagic\u003c/em\u003e E. coli. \u003cem\u003eETEC\u0026thinsp;=\u0026thinsp;enterotoxigenic\u003c/em\u003e E. coli. \u003cem\u003eSTEC\u0026thinsp;=\u0026thinsp;Shiga toxin-producing\u003c/em\u003e E. coli. \u003cem\u003eEIEC\u0026thinsp;=\u0026thinsp;Enteroinvasive\u003c/em\u003e E. coli. \u003csup\u003e\u003cem\u003e#\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eDAEC and EAEC were not included in the definition of any bacterial pathogen.\u003c/em\u003e ^When excluding EAEC and DAEC from pooled analysis (aRR 0.88, 95% CI 0.79\u0026ndash;0.98). We controlled for SES, access to at least basic sanitation, education level of the primary caregiver, child sex, time lived in household (months), employment status, and matching strata in all models (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData from children in intervention and comparison households revealed similar levels of any viral (30% vs 35%, aRR 0.85, 0.67\u0026ndash;1.07) and any helminthic infection (8% vs 7%, aRR not calculable; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Overall, we found that the intervention resulted in lower levels of individual pathogen infections. Notable findings include the impact of the intervention on EAEC among children in intervention (39%) versus comparison (48%) households (aRR 0.77, 95%CI 0.64\u0026ndash;0.93), and the impact on \u003cem\u003eCryptosporidium\u003c/em\u003e among children in intervention (11%) versus comparison (19%) arms (aRR 0.67, 95%CI 0.43\u0026ndash;1.03). A pooled estimate of all pathogens revealed a lower pathogen infection in intervention households than in comparison households (aRR 0.90 95%CI 0.81\u0026ndash;0.99; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB); a sensitivity analysis excluding EAEC and DAEC found similar results (aRR 0.88 95%CI 0.79\u0026ndash;0.98). We did not find evidence that the intervention substantially impacted enteric pathogen community profiles (PERMANOVA p-value 0.26) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Diarrhea was similar between arms (37% vs. 39%; aRR 0.99, 95%CI 0.80\u0026ndash;1.22). The intervention did not impact any growth outcomes \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Child mortality was not different in intervention (n\u0026thinsp;=\u0026thinsp;8) versus comparison households (n\u0026thinsp;=\u0026thinsp;10).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eDirect connection effect: Association with direct connection status\u003c/h3\u003e\n\u003cp\u003eThe prevalence of any bacterial or protozoal pathogen was 82% among households with a direct connection compared to 86% in households without (aRR 0.98, 95%CI 0.91\u0026ndash;1.04; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplemental Fig.\u0026nbsp;1\u003c/b\u003e). We similarly found no evidence of an association for the prevalence of any bacterial infection (aRR 1.00, 95%CI 0.91, 1.09) nor protozoal infection (aRR 0.91, 95%CI 0.72\u0026ndash;1.14). The prevalence of co-infection was 58% for children in households with a direct connection, and 63% for children in households without a direct connection (aRR 0.98, 95%CI 0.85\u0026ndash;1.13). Direct connection status was not associated with the number of pathogen infections (β -0.03, 95%CI -0.22, 0.07).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between direct connection to the water system at the household or compound and primary and secondary study outcomes among 12-month-old children enrolled in the PAASIM Study in Beira, Mozambique.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDirect household water connection\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;257)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo direct household water connection\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;373)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdjusted RR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary outcomes\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence of any bacterial or protozoan pathogen\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211 (82%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e320 (86%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98 (0.91, 1.04)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence of any bacterial infection\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e195 (76%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e293 (79%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.91, 1.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrevalence of any protozoan pathogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e74 (29%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e123 (33%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.91 (0.72, 1.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny co-infection (bacterial, protozoan, or viral pathogens) \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e149 (58%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e234 (63%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98 (0.85, 1.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdditional outcomes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny virus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (26%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e135 (36%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.82 (0.63, 1.06)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAny helminth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (7%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (8%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86 (0.49, 1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiarrhea (1-week period prevalence)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97 (37%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147 (39%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.99 (0.80, 1.22)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStunting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85 (23%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75 (0.48, 1.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33 (9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.72 (0.37, 1.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIndividual pathogens\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCampylobacter\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62 (24%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 (27%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04 (0.78, 1.38)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEAEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107 (42%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170 (46%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.03 (0.85, 1.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDAEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192 (75%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e268 (72%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.04 (0.95, 1.13)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etEPEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (20%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (21%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07 (0.74, 1.55)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaEPEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (37%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e136 (36%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (0.79, 1.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEHEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (1%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eETEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17 (7%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46 (12%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.65 (0.38, 1.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSTEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEIEC/Shigella\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39 (15%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (16%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.05 (0.66, 1.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eE. coli 0157\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (4%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC. difficile\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (10%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (5%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.45 (0.90, 2.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eV. cholerae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (1%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAstrovirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (5%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (6%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.69 (0.35, 1.35)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRotavirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (6%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (8%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.95 (0.49, 1.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdenovirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (5%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.91 (0.41, 1.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorovirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (7%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (13%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63 (0.40, 0.97)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSapovirus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (8%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38 (10%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.98 (0.60, 1.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCyclospora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEntamoeba histolytica\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGiardia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (16%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81 (22%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.77 (0.54, 1.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCryptosporidium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38 (15%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (15)%​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07 (0.80, 1.43)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAscaris lumbricoides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (4%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (4%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTrichuris trichiura\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (3%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (4%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAncyclostoma duodenale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNecator americanus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-​\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePooled effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.01 (0.94, 1.07)^\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathogen count\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (1.0)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (1.0)​\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03 (-0.22, 0.16)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLength-for-Age Z-score (LAZ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.88 (1.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.14 (1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04 (-0.25, 0.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight-for-Age Z-score (WAZ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.20 (1.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.64 (1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.22 (0.00, 0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eData are n (%) or mean (inter-quartile range). EAEC\u0026thinsp;=\u0026thinsp;enteroaggregative\u003c/em\u003e Escherichia coli. \u003cem\u003eDAEC\u0026thinsp;=\u0026thinsp;Diffusely-adherent\u003c/em\u003e E. coli. \u003cem\u003etEPEC\u0026thinsp;=\u0026thinsp;typical enteropathogenic\u003c/em\u003e E. coli. \u003cem\u003eaEPEC\u0026thinsp;=\u0026thinsp;atypical enteropathogenic\u003c/em\u003e E. coli. EHEC\u0026thinsp;=\u0026thinsp;\u003cem\u003eEnterohemorrhagic\u003c/em\u003e E. coli. \u003cem\u003eETEC\u0026thinsp;=\u0026thinsp;enterotoxigenic\u003c/em\u003e E. coli. \u003cem\u003eSTEC\u0026thinsp;=\u0026thinsp;Shiga toxin-producing\u003c/em\u003e E. coli. \u003cem\u003eEIEC\u0026thinsp;=\u0026thinsp;Enteroinvasive\u003c/em\u003e E. coli. \u003csup\u003e\u003cem\u003e#\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eDAEC and EAEC were not included in the definition of any bacterial pathogen.\u003c/em\u003e ^When excluding EAEC and DAEC from pooled analysis (aRR 0.96, 95% CI 0.85\u0026ndash;1.07). We controlled for SES, basic sanitation access, secondary education level of the primary caregiver, child sex, time living in household (months), fixed employment status, flooding in the household or yard and matching strata in all models (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData from children living in households with and without a direct connection revealed similar levels of any viral infection (26% vs 36%, aRR 0.82, 0.63\u0026ndash;1.06) and any helminthic infection (7% vs 8%, aRR 0.86, 95%CI 0.49\u0026ndash;1.52). We did not find any clear trend across associations between direct connection and individual pathogens, with the exception of norovirus, which was less prevalent in children living in households with a direct connection (7%) versus those without a direct connection (13%) (aRR 0.63, 95%CI 0.40\u0026ndash;0.97).We did not find evidence that having a direct connection substantially impacted pathogen community profiles (PERMANOVA p-value 0.31) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Data from children living in households with and without a direct connection revealed similar 1-week period-prevalence of diarrhea (37% vs. 39%, aRR 0.99, 0.80\u0026ndash;1.22). Having a direct connection was associated with higher weight-for-age Z-scores (WAZ; β 0.22, 95% CI 0.00-0.45), but not other measures of growth (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Child mortality was 7 deaths among households with a direct connection and 9 deaths among those without; 2 had unknown status.\u003c/p\u003e\n\u003ch3\u003eInteraction between study arm and direct connection status\u003c/h3\u003e\n\u003cp\u003eWe did not find a statistical interaction between study arm and direct connection status for our primary outcomes nor nearly all additional outcomes (\u003cb\u003eSupplemental Tables\u0026nbsp;2\u0026ndash;3\u003c/b\u003e), except for \u003cem\u003eEIEC/Shigella\u003c/em\u003e prevalence. However, for many outcomes we observed a trend toward a stronger magnitude of effect for the interaction between intervention study arms and direct connection status (\u003cb\u003eSupplemental Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eWe evaluated the impact of a large-scale urban water supply intervention among low-income neighborhoods in Beira, Mozambique that resulted in significant but modest improvements to water access and quality.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e We found inconsistent evidence for the impact of the intervention for individual outcomes; however, taken together the results suggest some evidence for modest impact of the water supply improvement intervention. Though we found no evidence that the intervention reduced the combined prevalence of any bacterial or protozoal infection, nearly all measured pathogens had protective point estimates, some of which conferred considerable benefit, although these estimates were imprecise. The pooled estimate across our pre-specified list of pathogens indicates that the intervention may have resulted in reductions in enteric infections. We found evidence that the intervention reduced pathogen co-infections, which appeared to be driven by co-infections of \u0026ge;\u0026thinsp;3 pathogens. We found no impact of the intervention on our secondary outcomes of diarrhea, mortality, or growth.\u003c/p\u003e\u003cp\u003eWe found no association between having a direct household connection (versus no direct connection) and our primary outcomes, and no associations with additional outcomes, with the exception of WAZ, which favored having a direct household connection. When we assessed the combined effect of being in the intervention arm and having a direct household connection, there was a trend of increased reduction in pathogen burden, suggesting that the indirect effects of a neighborhood-level intervention conferred benefit even to those without a household connection, potentially through reducing the localized force of infection.\u003c/p\u003e\u003cp\u003eThis study contributes important data to our understanding of if and how system-wide water system improvements impact early child gastrointestinal health. As a trial assessing the \u003cem\u003eeffectiveness\u003c/em\u003e of an intervention to improve urban water supply, our results do not question the potential \u003cem\u003eefficacy\u003c/em\u003e of improved water or household water access to reduce fecal pathogen exposure and improve child health. Indeed, elsewhere we report that the intervention led to improvements in microbial water quality (though not in stored water in the home), water access, satisfaction with water services, and water consumption rates.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eEarly infection and heavy pathogen burden are perhaps more important drivers of long-term health than maternally-reported child diarrhea period-prevalence. A reduction in co-infections could have important long-term health implications, as early and repeated infection can lead to growth shortfalls and gut dysbiosis. Early infection with \u003cem\u003eCryptosporidium\u003c/em\u003e, one of the pathogens most impacted by the intervention, is a particular challenge for child health.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This internal measure of exposure aligns with our findings on external measures of exposure,\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e that \u003cem\u003eCryptosporidium\u003c/em\u003e was detected in the water supply in Beira. \u003cem\u003eCryptosporidium\u003c/em\u003e is a protozoan largely resistant to chlorination, so our findings support the need to improve filtration systems and reduce the storage of water at the household to prevent recontamination. In addition, the association between direct connection and lower norovirus infections is intriguing given that a similar result was found for norovirus in the WASH-Benefits study in Bangladesh,\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e which calls into question the assumption that viruses are less likely waterborne.\u003c/p\u003e\u003cp\u003eThe findings reported here suggest that the water supply improvements studied were insufficient to confer substantial reductions in enteropathogen infection rates or other anticipated health gains, which are downstream on our theory of change. Our results should be considered within the context of several factors related to the study design, intervention fidelity, and contextual considerations. First, there may not have been enough intervention households connected to the upgraded network to have made a substantial impact on infection prevalence and other study outcomes. While the intervention did lead to a 34% increase in prevalence of household connections, only 46% of intervention households had direct connections (compared to 36% in control) at the 12-month visit. This biases the result toward the null.\u003c/p\u003e\u003cp\u003eSecond, as found elsewhere, water intermittency may have limited the potential benefit of the interventions.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e Water was available on average for 13 hours/day, requiring even those with direct connections to store water (99% of study households reported storing water). Water storage is well known to lead to recontamination,\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e suggesting the need for interventions that not only reduce water loss and improve source water quality, but also ensure continuous water supply and/or promote chlorination and safe storage at the household. Evidence of contamination from household water storage underscores the importance of achieving continuous supply.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e We found no previous studies that measured the impact of water supply improvements on pathogen infection. However, some did assess the impact on diarrhea and found mixed results. \u003cem\u003eErcumen et al\u003c/em\u003e hypothesized that in their trial in India,\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e the non-statistical \u003cem\u003eincrease\u003c/em\u003e in diarrhea among intervention households was due to water storage practices. Intermittency mitigated the impact of water supply improvements to confer reductions in diarrhea, or even exacerbated exposure, in data from Yemen,\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e and this is supported by findings in rural water supply as well.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The need to reduce water storage and recontamination to fully yield the potential gastrointestinal health benefits of water supply interventions is further highlighted by our findings that a direct household connection was not independently associated with our primary endpoints.\u003c/p\u003e\u003cp\u003eThird, the high force of infection among our study population means that even the delivery of high-quality drinking water would likely be insufficient to block transmission across other environmental pathways, such as flies, hands, food, and via domestic animals.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Our study was not powered for individual pathogens, which could be associated with different transmission pathways. While traditional sample size calculations dictate that the more prevalent an outcome, the more achievable a statistically significant result, this does not hold with many reoccurring infectious diseases that circulate within a study population. A reanalysis of the WASH-benefits studies in Kenya and Bangladesh revealed that lower pathogen baseline levels would add study power, as would increasing intervention coverage and efficacy to close pathways of infection.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e In data from India, researchers pointed to household water storage and \u0026ldquo;non-water\u0026rdquo; routes as drivers of their null results.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eFourth, system-wide improvements in Beira likely improved quality and pressure across all neighborhoods where water was delivered, reducing the exposure differences between intervention and comparison sub-neighborhoods, again potentially biasing our results towards the null. Water quality was high throughout the system: 55% of households had source-water free chlorine levels above 0.2 mg/L, the WHO-recommended guidelines, we found overall low levels of fecal indicator bacteria in the water,\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e and detected few pathogens in source water other than \u003cem\u003eCryptosporidium\u003c/em\u003e. Yet water intermittency resulted in high levels of water storage, and thus, contamination of water within the home. These system-level improvements reduce the importance of the drinking water route of transmission relative to other routes of exposure such as through hands, fomites, food, or animals.\u003c/p\u003e\u003cp\u003eAside from the limitations discussed above, there are considerable challenges with conducting an impact evaluation of a neighborhood-level water supply improvement. As evaluators, we had no control over the implementation of the intervention, the target neighborhoods, or the timing, so we had to work closely with the water utility to understand the timing and scope of the improvements. We also had to focus our research question to the area of the city where we could establish a robust counterfactual, with appropriate matching and controlling variables. Because we were not able to randomize sub-neighborhoods to the intervention, there remains some chance of unobserved confounding. It is functionally impossible to randomize a neighborhood-level infrastructure project, yet estimating the impact of these types of community-level interventions is of critical importance. Despite the inherent challenges, we were able to establish robust counterfactuals to investigate the impact of urban water supply infrastructure improvements on multiple child health outcomes. The inclusion of quasi-experimental evidence in the development of global burden of disease estimates has been prioritized by the WHO. Strengths of the study include the unique aspect of the design that allowed for examination of both neighborhood- and household-level effects, and the inclusion of enteric pathogens as internal measures of exposure.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThere is little doubt of the potential for broad-scale health, economic, and social benefits conferred by water system improvements, but in this study, improvements in child enteropathogen infection and other health outcomes were limited, potentially hindered by incomplete coverage of direct connections to the household or compound, and/or intermittent supply of the water system. For water supply investments to yield greater reductions in enteropathogen infection among young children, they likely need to reduce the need to store water by increasing system reliability and household connections or be coupled with interventions focused on safe storage. Further, it may be that in high burden settings such as Beira, water supply improvements alone may be insufficient to substantially reduce exposure to fecal pathogens.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eOur detailed protocol\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e and pre-analysis plan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/4rkn6/\u003c/span\u003e\u003cspan address=\"https://osf.io/4rkn6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) are reported elsewhere. The study protocol, informed consent forms, and data collection tools were approved by the Mozambique National Bio-Ethics Committee for Health (IRB00002657) and Emory University\u0026rsquo;s Institutional Review Board (IRB00098584).\u003c/p\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWe conducted a prospective matched-control study \u003cem\u003ePesquisa Sobre o Acesso \u0026agrave; \u0026Aacute;gua e a Sa\u0026uacute;de Infantil em Mo\u0026ccedil;ambique - Research on Access to Water and Children's Health in Mozambique\u003c/em\u003e (PAASIM) from 2021\u0026ndash;2023 in the coastal city of Beira, the second largest city in Mozambique. The primary aim of PAASIM was to assess the impact of large-scale water system improvements on acute and chronic health outcomes in children in low-income urban neighborhoods.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWe assessed the impacts of living in sub-neighborhoods with the improved water network compared to living in sub-neighborhoods without improvements (\u0026ldquo;Network Effect\u0026rdquo;). We also use this observational cohort to assess the association between having a direct household water connection compared to having no direct connection, without regard to intervention status (\u0026ldquo;Direct Household Connection Effect\u0026rdquo;).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eIntervention\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe World Bank invested US\u003cspan\u003e$\u003c/span\u003e200\u0026nbsp;million with the Mozambican public institutions FIPAG (the water utility) and AURA, IP (the water regulatory authority) through the Water Service \u0026amp; Institutional Support (WASIS-II) Project, starting in 2016. FIPAG leveraged this and additional funding to carry out improvements, including rehabilitation of water treatment facilities, replacing failing pipe mains, reticulation of water supply to new areas, improving service in areas with poor coverage or low water pressure, promoting water connections, institutional support for local water utilities, and emergency water supply systems repair. The stated goals in Beira included increasing flow rate by 15,000 m\u003csup\u003e3\u003c/sup\u003e/day, adding 25,000 new household connections, and increasing piped water supply to 16 hrs/day.\u003c/p\u003e\u003cp\u003eSome improvements (e.g., water treatment facility rehabilitation) impacted the entire city, while others affected only certain neighborhoods. For this evaluation, we focused on the water supply improvements in areas where a valid counterfactual was available that impacted marginalized populations. We worked closely with engineers at FIPAG to identify neighborhoods slated to receive improvements that had potentially comparable neighborhoods which would not be affected by the planned improvements to the water distribution system. Planned improvements in the intervention neighborhoods included new tertiary water networks linking water mains to households, designed to reduce water loss and reduce illegal connections, thereby increasing the water pressure and quality and increasing the system\u0026rsquo;s capacity for direct connections. Households in both intervention and comparison neighborhoods were able to connect to the FIPAG water system, but in the intervention neighborhoods, FIPAG promoted new connections, with connection costs covered by households.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eProcess indicators\u003c/h2\u003e\u003cp\u003eWe report elsewhere on an extensive set of process measures for the intervention, which showed modest impact of the intervention.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Notably the intervention increased water connections by 34% more connections (aRR 1.34, 95% CI 1.05\u0026ndash;1.72), an absolute difference of 10 percentage points (46% (139/302) of intervention households connected to the water network compared to 36% (110/309) in households in comparison neighborhoods). The intervention reduced source water \u003cem\u003eE. coli\u003c/em\u003e contamination by 33% and stored water \u003cem\u003eE. coli\u003c/em\u003e contamination by 14%. Having a direct connection was associated with a 24% decrease in households with source water \u003cem\u003eE. coli\u003c/em\u003e contamination compared to households without a direct connection, but there was no difference in stored water \u003cem\u003eE. coli\u003c/em\u003e contamination. Households in intervention neighborhoods and households with a direct connection experienced improved water availability, water pressure, and satisfaction with water services, though differences were modest.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eNeighborhood and household selection\u003c/h2\u003e\u003cp\u003eWith FIPAG, we identified sub-neighborhoods for recruitment and performed a population-based community survey in November-December, 2020 of ~\u0026thinsp;1,700 households. A socioeconomic status (SES) score was constructed using the Mozambique 'simple poverty scorecard',\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and scores aggregated at the sub-neighborhood level were categorized into tertiles. Housing density was assessed using random grid sampling using Google Earth imagery.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e We group-matched intervention and comparison sub-neighborhoods on SES and density using coarsened exact matching, resulting in 36 intervention (~\u0026thinsp;16,800 households) and 26 comparison (~\u0026thinsp;9,500 households) sub-neighborhoods.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eOutcomes\u003c/h2\u003e\u003cp\u003eThe primary outcomes included: prevalence of any bacterial and/or protozoal pathogen infection; each individual pathogen; and any co-infection at age 12 months. Viral pathogens were excluded from the primary analysis because waterborne transmission is unlikely to dominate for these pathogens.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e We excluded Enteroaggregative \u003cem\u003eEscherichia coli\u003c/em\u003e (EAEC) and Diffuse Adherent \u003cem\u003eE. coli\u003c/em\u003e (DAEC) because their etiology as pathogens is unclear. Other primary outcomes (source drinking water quality and gut microbiome composition at 12 months) will be reported elsewhere (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://osf.io/4rkn6/\u003c/span\u003e\u003cspan address=\"https://osf.io/4rkn6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additional outcomes include individual pathogens (separately and pooled), any viral infection, pathogen community similarity, diarrhea period-prevalence, child growth, and all-cause mortality.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSample size and power calculations\u003c/h2\u003e\u003cp\u003eOur sample size of 548 households\u0026ndash;\u0026ndash;allocated 1:1 in matched comparison sub-neighborhoods\u0026ndash;was powered based on the prevalence of any non-viral pathogen. Utilizing data from the MapSan trial for children 10\u0026ndash;14 months of age,\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e we used a comparison group prevalence of 70% for any non-viral pathogen, and estimated the ability to detect a relative risk of 0.74, alpha\u0026thinsp;=\u0026thinsp;0.05, power\u0026thinsp;=\u0026thinsp;80%, and an estimated intraclass correlation coefficient of 0.05. Our target enrollment of 900 mother-child dyads accounted for loss to follow-up.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eParticipant Recruitment\u003c/h2\u003e\u003cp\u003eWe selected the first 12 months of life because it is a critical development window when children are at high risk of acute and chronic effects of enteropathogen infection,\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e and it is a short enough period of time to avoid changes in water access that might occur. We identified eligible pregnant women through our population-based survey, local health center pre-natal records, study staff visits to under-enrolled sub-neighborhoods, and study participant referral. We used sub-neighborhood enrollment targets proportionate to our density estimates to achieve balance between arms.\u003c/p\u003e\u003cp\u003eDuring an initial pre-birth visit, we consented pregnant women who met our study eligibility: 1) 18 years or older, 2) in third trimester of pregnancy, 3) resides in enrolled study cluster, 4) not planning to move within the next 12 months, 5) carrying a singleton birth​. We re-assessed study eligibility at each follow-up visit. Households were considered lost to follow-up if participants withdrew from the study, were unreachable, or changed any eligibility status above (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We continued data collection if participants moved within the study area.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eData Collection\u003c/h2\u003e\u003cp\u003eAt each post-birth visit (at 3-, 6-, 9- and 12-months) we carried out a structured household survey and measured child length, weight, and head circumference, and calculated length-for-age and weight-for-age Z-scores. Prevalence of stunting and underweight were defined as two standard deviations below median of the reference population. We asked the caregiver to report diarrhea of the index child in the previous week. We recorded if a child died at any point after enrollment of the mother. Data were collected on electronic tablets using Open Data Kit (ODK) Collect. To facilitate communication with the study team, participants received a 150 MZN phone credit at each visit. No financial incentive was offered per Mozambican guidelines for human subjects research.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStool Collection and Laboratory Analysis\u003c/h2\u003e\u003cp\u003eChild stool was collected at post-birth household visits, as described elsewhere. Briefly, we placed three 1-mL aliquots in 9-mL temperature-stable lysis buffer (DNA/RNA Shield Fecal Collection Tubes, Zymo Research, Irvine, CA). INS staff also performed parasitological analysis and referred participants for deworming if applicable at the 12-month visit.\u003c/p\u003e\u003cp\u003eStool samples were extracted using the QIAamp Virus 96 QIAcube HT Kit (Qiagen, Germantown, MD) with modifications. We added 200 mL ASL lysis buffer, 0.004 \u0026micro;L ZymoBIOMICS Spike-in Control I (High Microbial Load) (Zymo Research), and 10 \u0026micro;L INFORCE 3 (Zoetis Inc, Kalamazoo, MI) to 1 mL samples. Samples were extracted in duplicate; the extracted nucleic acid duplicates were pooled and then divided into single use aliquots. Extracted nucleic acids were analyzed using the TaqMan Array card (TAC, ThermoFisher Scientific, Waltham, MA, USA), which allows quantification by real-time PCR via a 384-well microfluidic card for simultaneous detection of multiple viral, bacterial, and parasitic enteric pathogen targets as well as antimicrobial resistance genes,\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e customized for our targets of interest.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eWe used an intention-to-treat analysis for the Network Effect analysis: to compare children living in designated intervention versus comparison sub-neighborhoods, without regard to uptake/use of the intervention (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. We used multivariable log-linear binomial and modified Poisson generalized estimating equations (GEE) regression models; due to issues with model convergence, we used an independent correlation structure with robust standard errors to account for clustering and controlled for the matching strata. We additionally controlled for household- and individual-level confounders, including household SES, household sanitation, mother\u0026rsquo;s education-level, child sex, and others found to be imbalanced in our baseline assessment. We assessed the similarity of enteric pathogen community profiles between arms using PERMANOVA analysis of binomial presence/absence data for individual pathogens and plotted results using nonparametric multidimensional scaling (NMDS), a data reduction technique. For the Direct Connection Effect: to assess the association with having a direct connection at the household, we applied similar models to those described above, controlling for intervention arm. We also assessed the interaction between the household and sub-neighborhood network variables, to examine the joint effect of the intervention and the direct connection. We pooled the risk ratios of individual pathogens using meta-analysis, allowing us to account for trends in associations between the counterfactual comparisons and individual pathogen results. For analyses using measures across multiple time points, we accounted for clustering. We conducted sex-stratified analyses for outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAll lab personnel and field enumerators were blinded to the intervention status of the samples and households. Participants cannot be blinded to their household-level water exposure status or cluster-level exposure status, although they may not have been aware of water improvements in their sub-neighborhood. Two analysts blinded to the group assignments cleaned and independently replicated the analysis.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDECLARATION OF INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA SHARING.\u0026nbsp;\u003c/strong\u003eDeidentified participant data, replication analysis code, and data dictionary will be made available upon publication upon request for re-analysis, and for additional analyses with data sharing agreement via our OSF site: https://osf.io/4rkn6/\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNIAID R01AI130163. NIEHS T32ES012870\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCutler, D.; Miller, G. 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Health\u003c/em\u003e 2015, \u003cem\u003e3\u003c/em\u003e (9), e564\u0026ndash;e575. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2214-109X(15)00151-5\u003c/span\u003e\u003cspan address=\"10.1016/S2214-109X(15)00151-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6697339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6697339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe conducted a matched-control study in Beira, Mozambique to assess the impact of neighborhood-level urban water system improvements on child health and enteric infections. The intervention did not impact infection with bacteria (aRR 0.97, 95%CI 0.90-1.05) or protozoa (aRR 0.93, 95%CI 0.74-1.16), but did impact overall infection with individual pathogens (aRR 0.90, 95%CI 0.81-0.99) and co-infection prevalence (aRR 0.87, 95%CI 0.78-0.98). We found no association between direct household water connections – independent of intervention status – on prevalence of bacteria (aRR 1.00, 95%CI 0.91, 1.09), protozoa (aRR 0.91, 95%CI 0.72-1.14), or co-infection (aRR 0.98, 95%CI 0.85-1.13). We found no associations with diarrhea, child growth, or child mortality. Our evidence points to potential impacts of the intervention on enteric pathogen infections, but our estimates were imprecise. The impact of the intervention may have been limited by the lack of provision of continuous, reliable water supply, and lack of safe water storage.\u003c/p\u003e","manuscriptTitle":"The Impact of Urban Water Supply Improvements on Infant Enteric Pathogen Infection, Diarrhea, and Growth: Results from the PAASIM Matched Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 15:02:31","doi":"10.21203/rs.3.rs-6697339/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-water","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natwater","sideBox":"Learn more about [Nature Water](https://www.nature.com/natwater/)","snPcode":"44221","submissionUrl":"https://mts-natwater.nature.com/cgi-bin/main.plex","title":"Nature Water","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2df61ae1-1543-461a-9ebd-ee073cd86bbb","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":51583703,"name":"Health sciences/Diseases/Gastrointestinal diseases/Intestinal diseases/Diarrhoea"},{"id":51583704,"name":"Health sciences/Diseases/Infectious diseases/Bacterial infection"}],"tags":[],"updatedAt":"2026-02-18T11:26:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 15:02:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6697339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6697339","identity":"rs-6697339","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}