Effects of single and combined water, sanitation and hygiene ......pit latrines, VIP latrine or use...

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Effects of single and combined water,sanitation and hygiene (WASH)interventions on nutritional status ofchildren: a systematic review andmeta-analysisZemichael Gizaw1* and Alemayehu Worku2

Abstract

Background: Under nutrition is linked with poor water, sanitation and hygiene (WASH) condition. However, there isconflicting evidence on the effect of WASH on nutritional status of children. This review was, therefore, conductedto estimate the pooled effect of WASH interventions on child under nutrition.

Methods: All published and unpublished cluster-randomized, non-randomized controlled trials, and before andafter intervention studies conducted in developing countries were included. Relevant articles were searched fromMEDLINE/PubMed, Cochrane Collaboration’s database, Web of Science, WHO Global Health Library, Google Scholar,Worldcat and ProQuest electronic databases. The methodological quality of the included studies was assessedusing JBI critical appraisal checklist for randomized and non-randomized controlled trials. The risk of bias wasassessed using the Cochrane Collaboration’s tool for assessing risk of bias in randomized trials. The treatment effectwas expressed as standardized mean differences (SMD) with 95% confidence interval (CI).

Results: This meta-analysis of 10 studies including 16,473 children (7776 in the intervention and 8687 in the controlgroup) indicated that WASH interventions significantly associated with increased pooled mean height-for-age-z-score (SMD = 0.14, 95% CI = (0.09, 0.19); I2 = 39.3%]. The effect of WASH on HAZ was heterogeneous in age andtypes of interventions. WASH intervention had more effect on HAZ among under two children [SMD = 0.20,95% CI = (0.11, 0.29); I2 = 37%]. Children who received combined WASH interventions grew better compared withchildren who received single interventions [SMD = 0.15, 95% CI = (0.09, 0.20); I2 = 43.8%].

Conclusion: WASH interventions were significantly associated with increased mean height-for-age-z score in under5 years old children. The effect of WASH on linear growth is markedly different with age and types of interventions,either single or combined. Implementing combined WASH interventions has a paramount benefit to improvenutritional status of children.

Keywords/phrases: WASH interventions, Nutritional status of children, Mean height-for-age-z score, Under fivechildren, And developing countries

© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

* Correspondence: [email protected] of Environmental and Occupational Health and Safety, Instituteof Public Health, College of Medicine and Health Sciences, University ofGondar, Gondar, EthiopiaFull list of author information is available at the end of the article

Gizaw and Worku Italian Journal of Pediatrics (2019) 45:77 https://doi.org/10.1186/s13052-019-0666-2

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BackgroundChildren are the most vulnerable group to a widerange of infections unless special attention is given.The highest proportions of infections among childrenare poor WASH related diarrheal and parasitic dis-eases. Globally, there are nearly 1.7 billion cases ofchildhood diarrheal disease every year and diarrhea isresponsible for killing around 525,000 children everyyear [1]. About 3.5 billion people (the majority ofthese cases were children) in the world were infectedwith intestinal parasites caused by helminthes andprotozoa during 2009 [2].Repeated exposure to diarrheal and parasitic infections

causes environmental enteropathy (EE) or sometimescalled environmental enteric dysfunction (EED). EE is aninflammatory condition of the gut of children which ischaracterized by villous atrophy, crypt hyperplasia, in-creased permeability, inflammatory cell infiltrate, andmodest malabsorption [3–5].Diarrhea, parasitic infections and EE are key medi-

ating pathways linking poor WASH to developmentaldeficit [6–8]. A large body of evidence suggests thatmalnutrition is linked with poor WASH practice [9–12]. Poor WASH is associated with under nutrition asa result of diarrhea, nematode infection and EE. Diar-rhea and intestinal worms cause nutrient losses anddiversion of nutrients from growth to the immunesystem to fight the infection [13–19]. EE increasesthe small intestine’s permeability and reduces nutrientabsorption [20–24]. The link of poor WASH andunder nutrition has also socio-economical mechanism.For instance the energy cost of carrying water forlong distances from the source to home. The averagewoman carrying a typical load of 20 l on level groundwould consume about 39 cal per kg of body weightper hour with an assumption that 1 g of maize mealyields 3.5 cal [25].WASH interventions are the most holistic and ef-

fective approaches to prevent stunting and wastingamong under two children. However, there is conflict-ing evidence on the effect of WASH on lineargrowth. Some studies reported that there is no signifi-cant association between WASH and linear growth[26–30] and some others reported that WASH hassignificant effect [8, 31–33]. This systematic reviewand meta-analysis was, therefore, aimed to estimatethe pooled effect of WASH on linear growth amongunder five children.

MethodsResearch questionDoes access to improved WASH facilities have effect onchild growth?

Criteria for considering studies for this reviewTypes of studiesAll published and unpublished community-based trials(including cluster-randomized, quasi-randomized, non-randomized controlled trials, controlled before and afterintervention studies) conducted in developing countriesto analyze the effect of WASH on the mean height-for-age- z score were included. Studies published in Englishlanguage in the last 10 years were also included. Cita-tions with no abstracts and/or full texts, duplicate stud-ies, and studies with poor quality were excluded.

Types of participantsChildren aged under 5 years.

Types of interventions

1. Any intervention aimed at improving themicrobiological quality of drinking water, includinghousehold and community level water treatment,water source protection and household waterhandling.

2. Interventions aimed to reduce direct and indirectcontact with human faeces (pour flush, water sealedflush toilet, piped sewer system, septic tank, simplepit latrines, VIP latrine or use off scoop for thedisposal of child faeces).

3. Interventions aimed at the promotion of handwashing with soap or ash after defecation, disposalof child faeces and prior to preparing and handlingfood

4. Any WASH promotion aimed at WASH behavioralchange like community-led total sanitation (CLTS)

5. Any combination of the WASH interventions listedabove

Control

1. Water quality: participants who continued withusual practice, or a less stringent version of theintervention (i.e. new protected well but nohousehold disinfection).

2. Sanitation: participants who continued with usualpractice rather than following the prescribedintervention.

3. Hygiene: no hand washing promotion andparticipants who continued with usual practice.

4. WASH promotion: participants who did not receiveWASH behavioral change education

Types of outcome measuresChild nutritional status or stunting measured by meanheight-for-age-z score.

Gizaw and Worku Italian Journal of Pediatrics (2019) 45:77 Page 2 of 14

Search strategy and study selectionWe searched relevant articles from MEDLINE/PubMed,Cochrane Collaboration’s database, Web of Science,WHO Global Health Library, Google Scholar, Worldcatand ProQuest. A three-step search strategy was utilizedin this review. An initial limited search of MEDLINEwas undertaken followed by analysis of the text wordscontained in the title and abstract, and of the indexterms used to describe articles. A second search usingall identified keywords and index terms was undertakenacross all included databases. Thirdly, references of allidentified articles were searched to get additional studies.The below box shows terms used to search literatures.

(((“child”[MeSH Terms] OR “child”[All Fields] OR “children”[All Fields]) OR(“child”[MeSH Terms] OR “child”[All Fields])) AND ((((((((“sanitation”[MeSHTerms] OR “sanitation”[All Fields]) OR “Wastemanagement”[All Fields]) OR“Latrine utilization”[All Fields]) OR “Water quality”[All Fields]) OR “Foodhygiene”[All Fields]) OR “Personal hygiene”[All Fields]) OR “Handwashing”[All Fields]) OR “Water, sanitation and hygiene”[All Fields])) AND((((((((“malnutrition”[MeSH Terms] OR “malnutrition”[All Fields] OR“undernutrition”[All Fields]) OR (“malnutrition”[MeSH Terms] OR“malnutrition”[All Fields])) OR (“growth disorders”[MeSH Terms] OR(“growth”[All Fields] AND “disorders”[All Fields]) OR “growth disorders”[AllFields] OR “stunting”[All Fields])) OR “Linear growth deficit”[All Fields]) OR“Growth faltering”[All Fields]) OR “Growth failure”[All Fields]) OR “Growthimpairment”[All Fields]) OR “Growth disorder”[All Fields])

Assessment of methodological qualitySearch results from different electronic databases wereexported to Endnote reference manager to removeduplication. We screened out articles using titles andabstracts. We further investigated and assessed full-textarticles against the inclusion and exclusion criteria. Themethodological quality of the included studies wasassessed using JBI critical appraisal checklist forrandomized and non-randomized controlled trials(Additional file 1) [34].

Data extractionWe independently extracted data from papers includedin the review using the JBI standardized data extractiontool (Additional file 2). The data extraction form waspiloted on randomly selected papers and modifiedaccordingly. Eligibility assessment was performedindependently by the two reviewers. We invested ourmaximum effort to avoid introduction of errors (e.g.entering wrong numerals into a spreadsheet and failureto identify required data from a study report) or biasduring extraction. Data were systematically extractedrelating to the nature of studies.

Assessment of risk of bias in included studiesWe independently assessed the risk of bias of includedstudies using the Cochrane Collaboration’s tool forassessing risk of bias in randomized trials [35]. Random

sequence generation (selection bias), allocationconcealment (selection bias), blinding of participantsand personnel (performance bias), blinding of outcomeassessment (detection bias), incomplete outcome data(attrition bias), selective reporting (reporting bias) andother possible risk of bias (buffer to preventinformation contamination and measure or adjustpotential confounders) were items used to assess risk ofbias. We assessed studies for each item with answers of‘low’ indicating low risk of bias, ‘high’ indicating highrisk of bias and ‘unclear’ indicating either lack ofinformation or uncertainty over the potential for bias(Additional file 3).

Measures of treatment effectStata version 11 was used to measure treatment effectand to conduct other analysis. We expressed treatmenteffect sizes as SMD with 95% CI using their originalscale. We used forest plot to present results.

Sensitivity analysisSensitivity analysis was carried out to see the impact ofindividual studies for the pooled result.

Assessment of heterogeneityWe assessed statistical heterogeneity using the I2

statistics. Galbraith plot was also used to observeheterogeneity.

Assessment of publication biasFunnel plot was used to see publication bias subjectivelyand Egger’s test was used to objectively check potentialpublication bias.

Dealing with missing dataWe contacted authors of potentially relevant studies thatdid not report the outcome and asked them to provideinformation on the availability of outcome data. Articleswith incomplete data were excluded from the meta-analysis while they were included in the systematicreview.

ResultsThe search processThe search strategy identified 815 titles and abstracts(663 from PubMed and 152 from other sources)published before 11 August 2018. We obtained 782articles after we removed duplicated articles. Followingassessment by title and abstract, 153 articles wereretrieved for more evaluation. Twelve articles wereincluded for systematic review and 10 articles wereincluded for meta-analysis based on the inclusion cri-teria. Of the 12 included reports, 9 were published injournals, one was a CEDLAS report, one was a Feinstein


International Center report and one was dissertation. Allof the included studies were published in English. Thestudy selection process is outlined in Fig. 1.

Description and characteristics of included studiesTwo studies [Arnold, 2009 and Langford, 2011] werenon-randomized trial and the rest were randomized trial.All included studies were conducted in low-income ormiddle income country settings. Two studies were con-ducted in Mali [Alzua, 2015 and Pickering, 2015], two inUganda [Marshak, 2015 and Muhoozi, 2017], two inIndia [Clasen, 2014 and Patil, 2014], One in Bangladesh[Shafique, 2013], one in Kenya [Arnold, 2018], one inPakistan [Bowen, 2012], one in Guatemala [Arnold,2009], one in Cambodia [McGuigan, 2011] and one inNepal [Langford, 2011].Intervention duration was ranged from 6 to 36months.

Three interventions were implemented for 6 months[Muhoozi, 2017; Marshak, 2015 and Langford, 2011], Oneintervention for 9 months [Bowen, 2012], one interventionfor 10 months [Alzua, 2015], two interventions for 12months [Shafique, 2013 and McGuigan, 201], one

intervention for 13months [Clasen, 2014], one interventionfor 17months [Pickering, 2015], one intervention for 21months [Patil, 2014], one intervention for 24months[Arnold, 2018], and one intervention for 36months [Arnold,2009]. Two studies [Alzua, 2015 and Pickering, 2015]implemented CLTS, one study [Patil, 2014] implementedtotal sanitation campaign (TSC), one study [Arnold, 2018]implemented WASH and nutrition, two studies [Marshak,2015 and Muhoozi, 2017] implemented nutrition and foodhygiene, one study [Arnold, 2009] implemented watertreatment and hand washing, one study [Shafique, 2013]implemented nutrition, health and hygiene education andhand hygiene, two studies [Bowen, 2012 and Langford, 2011]implemented hand washing, one study [McGuigan, 2011]implemented home based water treatment, and one study[Clasen, 2014] implemented latrine construction andpromotion.Height-for-age-z score was reported in all studies.

Except two studies [McGuigan, 2011 and Arnold, 2018],all others measured weight-for-age z-scores, five studies[Alzua, 2015; Arnold, 2018; Marshak, 2015; Pickering,2015 and Shafique, 2013] reported stunting, three studies

7 of the articles were excluded because they didn’t measure the outcome variable well

Iden

tific

atio

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Records identified through PubMed data base search (n =663 )

Record identified from other sources (n = 152)

Total articles found (n = 815)

Records after duplicates removed (n = 782)

Number of titles and/or abstracts screened (n = 153)

Number of obviously irrelevant reports excluded (n = 629)

Scre

enin

g

Full-text articles assessed for eligibility (n = 19)

Full-text articles excluded Book = 2 Review = 16 Cross-sectional = 82 Case control = 6 Cohort = 14Not discussed the outcome variable = 15

Elig

ibili

ty

Incl

uded

Studies included in the systematic review (n = 12)

Studies included in the meta-analysis (n = 10)

2 of the articles were excluded because of problem on reporting

Fig. 1 Study selection flow diagram


[Alzua, 2015; Marshak, 2015 and Pickering, 2015] re-ported underweight, three studies [Muhoozi, 2017; Patil,2014 and Arnold, 2009] reported mid upper arm circum-ference z-scores, one study [Muhoozi, 2017] reportedhead circumference z-scores, two studies [Bowen, 2012and Patil, 2014] reported body mass index, nine studies[Alzua, 2015; Arnold, 2018; Bowen, 2012; Clasen, 2014;Pickering, 2015; Arnold, 2009; Langford, 2011; McGuigan,2011; and Shafique, 2013] reported diarrhea, five studies[Alzua, 2015; Pickering, 2015; Arnold, 2009; Langford,2011 and Shafique, 2013] reported respiratory illness, twostudies [Clasen, 2014 and Patil, 2014] reported STHs, onestudy [Patil, 2014] reported anemia, three studies [Arnold,2018; Bowen, 2012 and Muhoozi, 2017] reported child de-velopment quotients, and one study [Arnold, 2018] re-ported markers of EE.

Risk of bias in included studiesAllocationWith the exclusion of the two non-randomized studies[Arnold, 2009 and Langford, 2011], we judged sequencegeneration is adequate in all other studies. All otherstudies allocated the intervention and control groupsrandomly. Random number generator [Arnold, 2018;Bowen, 2012 and Pickering, 2015], computer generatedsequence [Clasen, 2014 and Shafique, 2013] and rafflesystem [McGuigan, 201]) were used to generate randomsequence. However, allocation concealment was not pos-sible in all studies except Bowen, 2012.

BlindingFour of the include studies [Alzua, 2015; Bowen, 2012;Clasen, 2014 and Muhoozi, 2017] made studyparticipants or personnel blind and we judged thesestudies to be at low risk of performance bias. Alzua,2015; Arnold, 2018; Bowen, 2012; Clasen, 2014 andPickering, 2015 concealed group allocation frompersonnel who measured outcome variables in and thusjudged to be at low risk of detection bias.

Incomplete outcome dataThere was lose of follow-up in all studies, however themajority of studies [Alzua, 2015; Arnold, 2018; Clasen,2014; McGuigan, 2011; Muhoozi, 2017; Patil, 2014;Pickering, 2015 and Shafique, 2013] balanced participantsin both arms and conducted intention-to-treat analysis tomanage attrition bias and we judged these studies to be atlow risk of attrition bias.

Selective reportingWe assessed the trial registries and protocols of sixstudies; Arnold, 2018 and McGuigan, 2011 were notreported as per protocol. We found Alzua, 2015; Bowen,

2012; Patil, 2014 and Shafique, 2013 reported as perprotocol.

Other potential sources of biasWe assessed authors’ investment to prevent informationcross contamination among clusters and potential effectof confounders. Alzua, 2015; Marshak, 2015 andPickering, 2015 used buffer to prevent informationcontamination and Patil, 2014 and Shafique, 2013measured and adjusted anticipated confounders (seeFigs. 2 and 3 for the risk summary of studies).

Effects of WASH interventions on linear growthDifferent community-based randomized and non-randomized trials discussed the effect of single or/andcombined WASH interventions on child linear growth(expressed as mean height-for-age-z score in this study).The effect of WASH interventions on mean height-for-age-z score is summarized below.Alzua, 2015 was a cluster-randomized controlled trial

conducted among 7328 (3564 in the control arm and3764 in the intervention arm) children in Mali. Thisstudy reported that children in CLTS villages were taller(0·18 increase in height-for-age- z score, 95% CI 0.03–0.32) and less likely to be stunted (PR 0·86, 95% CI0·74–1·0) compared to children in the control arm.Arnold, 2009 was a non-randomized intervention conducted

among 877 children under 5 years of age in Guatemala.Compared to children in the control, a water quality and handwashing intervention had no effect on weight- for-age- z score(MD=−0.053, 95% CI= (− 0.331, 0.206), weight-for-height-zscore (MD - 0.066, 95% CI= (−0.248, 0.124), height-for-age- zscore (MD=0.041, 95% CI= (− 0.305, 0.326) or mid-upper armcircumference (cm) (MD=− 0.014, 95% CI= (−0.166, 0.145).Arnold, 2018 was a cluster-randomized controlled trial

conducted in Kenya among 2101children (364 in theintervention arm and 1737 in the control arm) and inBangladesh among 712 children (199 in the interventionarm and 513 in the control arm). The study reportedthat children who had access to improved latrine weretaller [MD = 0·15, 95% CI = (0·02–0·28] in Kenya and[MD = 0·22, 95% CI = (0·03–0·40)] in Bangladesh.Bowen, 2012 was a cluster-randomized controlled trial

conducted in Pakistan among 461 children (160 in thecontrol group, 141 in the hand washing group and 160in hand washing and water treatment group). This studyfound that 24.9% (95% CI, 20.0–30.6%) and of childrenhad z scores that were more than 2 SDs below the ex-pected z score for height for age which did not differ sig-nificantly across study groups.Clasen, 2014 was a cluster-randomized controlled trial

conducted in India among 24, 969 individuals in interven-tion villages and 25, 982 individuals in control villages.The study reported that the mean height-for-age-z-score


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Fig. 2 Risk of bias summary: review authors’ judgments about each risk of bias item for each included study

Random sequence generation (selection bias)

Allocation concealment (selection bias)

Blinding of participants and personnel (performance bias)

Blinding of outcome assessment (detection bias)

Incomplete outcome data (attrition bias)

Selective reporting (reporting bias)

Other bias

0% 25% 50% 75% 100%

Low risk of bias Unclear risk of bias High risk of bias

Fig. 3 Risk of bias graph: review authors judgment about each risk of bias item presented as percentages across all included studies


was not significantly differ between children from house-holds with functional latrine and with no functional latrine[MD= − 0·06, 95% CI = (− 0·27 to 0·15)].Langford, 2011 was a non-randomized controlled

trial study among 88 children under 1 year of age inNepal to see the effect of hand washing on subclinicalinfections and growth. The study reported that handwashing intervention had no effect on weight-for-agez-score (MD = − 0.24, 95% CI = (− 0.76, 0.28), weight-for-height z-score (MD = − 0.11, 95% CI = (− 0.53,0.31) or height-for-age z-score (MD = − 0.13, 95% CI =(− 0.54, 0.28).Marshak, 2015 was a randomized control trial

conducted in Uganda to see the impacts of a programentitled “Community Resilience to Acute Malnutrition”.This study included 1762 children in the control group(647 children in the baseline, 572 children in themidline, 543 in the endline) and 1656 children in theWASH intervention group (614 children in the baseline,555 children in the midline, and 487 in the endline). Thereport of the assessment indicated that children living inintervention settlements were less likely to be stuntedcompared to children living in non-intervention settle-ments. This relationship is further supported by a sig-nificant difference between intervention (HAZ = − 1.07)and non-intervention (HAZ = − 1.27) settlements.McGuigan, 2011 was a cluster-randomized controlled

trial conducted among 928 children (nutrition outcomedata were available for 760 children) under 5 years ofage in Cambodia to the impact of solar disinfection ofdrinking water on childhood diarrhea. Compared to chil-dren in the control arm, a water quality (SODIS) inter-vention had no effect on weight-for-age- z score (MD =0.26, 95% CI = (− 0.01, 0.53), weight-for-height- z score(MD = 0.15, 95% CI = (− 0.15, 0.45) or height-for-age- zscore (MD = 0.22, 95% CI = (− 0.04, 0.48).Muhoozi, 2017 was a community-based, open cluster-

randomized trial conducted in Uganda among 511 chil-dren aged 6–8 months (263 in the intervention groupand 248 in the control group). This study revealed thatthere was no evidence of a difference in mean length-for-age- z score at 20–24months between the two studygroups [MD = 0.10, 95% CI = (− 0.17, 0.36)].Patil, 2014 was a cluster-randomized controlled trial

conducted in India among 5209 children (2609 in thecontrol arm and 2600 in the intervention arm) to seethe effect of India’s total sanitation campaign ondefecation behaviors and child health. The studydepicted that the intervention had no significant effecton length/height-for-age-z score (HAZ in the controlgroup = − 1.38 and in the intervention group = − 1.81).Pickering, 2015 was a cluster-randomized controlled

trial conducted in Mali to see the effect of CLTS onchild diarrhea and child growth. Researchers enrolled

2365 households to receive the CLTS intervention and2167 households to the control group. The finding ofthe study showed that children in CLTS villages weretaller (0·18 increase in height-for-age-z score, 95% CI0·03–0·32) and less likely to be stunted (35% versus 41%,PR 0·86, 95% CI 0·74–1·0) than children in controlvillages.Shafique, 2013 is a randomized controlled trial

conducted in Bangladesh to measure the relative effectof directed use of benzalkonium chloride-containing,water-based hand sanitizers and broad-range multiplemicronutrient powder (MNP) along with nutrition,health and hygiene education (NHHE) to prevent infec-tions and linear growth faltering reported that combinedintervention of directed hand-sanitizer use and micronu-trient powder along with NHHE significantly improvedlinear growth of low birth weight infants compared toNHHE alone.This systematic review and meta-analysis of 10 studies

including 16,473 children (7776 in the interventiongroup and 8687 in the control group) indicated that sin-gle or/and combined WASH interventions significantlyassociated with increased mean height-for-age-z-score(SMD = 0.14, 95% CI = (0.09, 0.19); I2 = 39.3%] (Fig. 4).

Assessment of heterogeneityThe I2 statistics of the aggregate pooled estimate(I2 = 39.3%) showed moderate heterogeneity. As a result,Galbraith plot was used to observe heterogeneity. Theplot indicated that Patil S, 2014 caused the heterogeneity(Fig. 5).

Subgroup analysisA subgroup analysis by age showed that single or/andcombined WASH intervention had more effect on meanheight-for-age-z score among under two children comparedwith under 5 years old children[SMD=0.20, 95% CI = (0.11,0.29); I2 = 37%] (Fig. 6).A subgroup analysis by type of WASH interventions

(single interventions and combined interventions) wasdone. The result of the analysis showed that combinedWASH interventions significantly associated withincreased mean height-for-age-z score. Children who re-ceived combined WASH interventions grew better com-pared with children who received single interventions[SMD = 0.15, 95% CI = (0.09, 0.20); I2 = 43.8%] (Fig. 7).

Sensitivity analysisThe sensitivity analysis test result showed that thepooled effect of WASH intervention on the meanheight-for-age-z score was 0.19 [SMD = 0.19, 95% CI =0.15, 0.23]. There are studies which become out of thepooled result when they are out of the analysis. Themaximum impact for these studies was from 0.13 to


NOTE: Weights are from random effects analysis

Overall (I-squared = 39.3%, p = 0.096)

Arnold (2018)

Langford (2011)

Muhoozi (2017)

ID

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Arnold (2009)

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0.14 (0.09, 0.19)

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0.40 (0.14, 0.66)

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0.40 (0.14, 0.66)

0.02 (-0.11, 0.16)

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Fig. 4 Forest plot of comparison: height-for-age-z score (all community-based trial studies)

Fig. 5 Galbraith plot for heterogeneity


0.26, which is a 95% CI. There was no any significantdifference of each study for the total pooled result (Fig. 8).

Publication biasThe funnel plot (Fig. 9) and Egger’s test (Table 1) showthat there was no publication bias. The funnel plot issymmetric and p value for Egger’s test is 0.988.

DiscussionSummary of evidenceTwelve studies are included in this review. The reviewincluded ten community-based cluster-randomized con-trolled trials and two non-randomized community basedtrials. Studies included various water, sanitation and hy-giene (WASH) interventions either singly or in combin-ation. Most of the included studies were considered tobe at low risk of bias. Height-for-age-z-score, the pri-mary outcome variable of this review was available fromall studies. However, two of the studies didn’t report SDfor the mean height. As a result, the meta-analysis waslimited to data from ten studies.In this systematic review and meta-analysis, WASH in-

terventions significantly associated with increasedheight-for-age z-scores in children. The report of this

review is consistent with the reports of other systematicreviews. For instance, a systematic review and meta-analysis which included seven RCT comparators re-ported that WASH intervention was marginally associ-ated with mean height-for-age-z score (MD = 0.08, 95%CI = 0.00–0.16)) [36]. Similarly, a review conducted onthe impact of combined WASH intervention on growth,non-diarrheal morbidity and mortality in children resid-ing in low and middle-income countries found thatWASH interventions improved height-for-age-z scores(MD = 0.22; 95% CI 0.12, 0.32) and decreased the risk ofstunting by 13% (RR = 0.87; 95% CI = 0.81, 0.94)) [37].Other individual studies also reported that WASH inter-ventions were significantly associated with children’sheight [38–41]. The link of WASH and increased meanheight-for-age-z score is due to improved WASH condi-tion prevents diarrhea and parasitic infections whichcould cause reduced absorption and nutrient losses, re-duced appetite, and diversion of energy and nutrientsfrom growth to the immune system to fight the infection[13–19]. Improved WASH prevents EE which increasesthe small intestine’s permeability and reduces nutrientabsorption [20–24].This systematic review and meta-analysis indicated

that WASH interventions are more effective to improve


.

.


Alzua (2015)

ID

Subtotal (I-squared = 37.0%, p = 0.174)

Arnold (2018)

Shafique (2013)

Patil (2014)

Bowen (2012)

McGuigan (2011)

Muhoozi (2017)

Under two

Langford (2011)

Study

Arnold (2009)

Pickering (2015)

Under five


0.14 (0.09, 0.19)

0.14 (0.10, 0.19)

SMD (95% CI)

0.20 (0.11, 0.29)

0.23 (0.12, 0.34)

0.40 (0.14, 0.66)

0.21 (0.11, 0.30)

-0.01 (-0.24, 0.21)

0.12 (-0.02, 0.27)

0.10 (-0.08, 0.27)

-0.13 (-0.55, 0.29)

0.02 (-0.11, 0.16)

0.14 (0.06, 0.22)

0.12 (0.08, 0.17)

100.00

23.97

Weight

37.23

11.61

3.13

14.85

4.07

8.48

6.33

1.32

%

9.45

16.79

62.77

0.14 (0.09, 0.19)

0.14 (0.10, 0.19)

SMD (95% CI)

0.20 (0.11, 0.29)

0.23 (0.12, 0.34)

0.40 (0.14, 0.66)

0.21 (0.11, 0.30)

-0.01 (-0.24, 0.21)

0.12 (-0.02, 0.27)

0.10 (-0.08, 0.27)

-0.13 (-0.55, 0.29)

0.02 (-0.11, 0.16)

0.14 (0.06, 0.22)

0.12 (0.08, 0.17)

100.00

23.97

Weight

37.23

11.61

3.13

14.85

4.07

8.48

6.33

1.32

%

9.45

16.79

62.77

0-.5 0 .5

Fig. 6 Forest plot of comparison: height-for-age-z score (subgroup analysis by age)



.

.


Langford (2011)

ID

Pickering (2015)


Bowen (2012)

McGuigan (2011)

Arnold (2018)

Arnold (2009)

Shafique (2013)

Patil (2014)

Alzua (2015)

Muhoozi (2017)


Combined

Single

Study

0.14 (0.09, 0.19)

-0.13 (-0.55, 0.29)

SMD (95% CI)

0.14 (0.06, 0.22)

0.15 (0.09, 0.20)

-0.01 (-0.24, 0.21)

0.12 (-0.02, 0.27)

0.23 (0.12, 0.34)

0.02 (-0.11, 0.16)

0.40 (0.14, 0.66)

0.21 (0.11, 0.30)

0.14 (0.10, 0.19)

0.10 (-0.08, 0.27)

0.12 (-0.01, 0.25)

100.00

1.32

Weight

16.79

74.52

4.07

8.48

11.61

9.45

3.13

14.85

23.97

6.33

25.48

%

0.14 (0.09, 0.19)

-0.13 (-0.55, 0.29)

SMD (95% CI)

0.14 (0.06, 0.22)

0.15 (0.09, 0.20)

-0.01 (-0.24, 0.21)

0.12 (-0.02, 0.27)

0.23 (0.12, 0.34)

0.02 (-0.11, 0.16)

0.40 (0.14, 0.66)

0.21 (0.11, 0.30)

0.14 (0.10, 0.19)

0.10 (-0.08, 0.27)

0.12 (-0.01, 0.25)

100.00

1.32

Weight

16.79

74.52

4.07

8.48

11.61

9.45

3.13

14.85

23.97

6.33

25.48

%

0-.5 0 .5

Fig. 7 Forest plot of comparison: height-for-age-z score (subgroup analysis by intervention)

Fig. 8 Sensitivity analysis to assess the impact of individual studies to the mean height-for-age-z score pooled estimate


childhood nutrition among under two children com-pared with interventions in under five children. Thismight be due to the fact that the first 2 years of life arethe window to child’s future. Exposed to sanitation re-lated diarrheal and intestinal parasitic infections in thefirst 1000 days of a child’s life causes irreversible, long-term damage to a child’s health. Nutritional interven-tions during this time [42, 43].We found that combined WASH interventions were

more effective to improve child nutritional status thansingle interventions. This can be justified that theexposures to faecal-oral pathogens through drinkingwater, sanitation or hygiene, which is the mediatingpathway to WASH and nutrition is complex. Handwashing or treatment of drinking water or food safety or

sanitation alone cannot prevent the occurrence offaecal-oral diseases. The integration of interventions iseffective to prevent infections and to promote nutritionstatus [44, 45].

Applicability of evidenceThere is suggestive evidence from cluster-randomizedcontrolled trials on the effect of WASH interventions onnutritional status of children. However, there is limitedevidence on the effect of WASH interventions on nutri-tional outcomes in children. We believe, the result ofthis meta-analysis provides supportive evidence on theeffect of WASH interventions on nutritional status ofchildren. The finding of this review provides evidencefor policy makers, health practitioners, WASH and

0.0

5.1

.15

.2.2

5S

EM

-.2 0 .2 .4 .6MD

Funnel plot with pseudo 95% confidence limits

Fig. 9 Funnel plot with pseudo 95% CI to show publication bias

Table 1 Egger's test showing objectively testing of bias


nutrition advocators to enhance child health, child nutri-tion and sanitation condition, and to prevent sanitationrelated infectious diseases. Further large scale studies de-signed to measure the impact of WASH interventionson nutritional outcomes in children are needed.

LimitationWe entirely relied on freely access electronic databasesto search relevant articles. We didn’t include articlesavailable in hard copy. During the process of the review,we identified one study that collected but did not reportnutritional outcomes in children. It is possible that otherstudies which we did not identify collected data on childnutritional status and WASH that we have been unableto include in this review. Due to these, the searchingwas not exhaustive. We believed we could get morerelevant articles if we had access to other databases andhard prints.

ConclusionWASH interventions were significantly associated withincreased mean height-for-age-z score in under 5 yearsold children. The effect of WASH on linear growth ismarkedly different with age and types of WASH inter-ventions, either single or combined. Implementing com-bined WASH interventions has a paramount benefit toimprove nutritional status of children.

Additional files

Additional file 1: JBI critical appraisal checklist for randomizedcontrolled trials. (DOCX 31 kb)

Additional file 2: Data extraction format. (DOCX 35 kb)

Additional file 3: Criteria for judging risk of bias in the ‘Risk of bias’assessment tool. (DOCX 59 kb)

AbbreviationsCI: Confidence interval; CLTS: Community leading total sanitation; c-RCT: Clustered randomized controlled trial study; HAZ: Height-for-age-zscore; HS: Hand sanitizers; JBI: The Joanna Briggs Institute; MD: Meandifference; MNP: Multiple micronutrient powder; NHHE: Nutrition, health andhygiene education; RCT: Rndomized controlled trial; SD: Standard deviation;SMD: Standardized mean difference; TS: Total sanitation; VIPlatrine: Ventilated improved latrine; WASH: Water, sanitation and hygiene;WAZ: Weight-for-age-z score; WHO: World health organization

AcknowledgmentsThe authors would like to thank Dr. Mekuriaw Alemayehu, a faculty memberof the University of Gondar for his participation throughout the reviewprocess.List of articles included in the reviewAluza, 2015 (unpublished)Alzua ML, Pickering AJ, Djebbari H, Lopez C, Cardenas JC, Lopera MA, et al.Impact Evaluation of Community-led Total Sanitation (CLTS) In Rural Mali. Afinal report from from a cluster randomized controlled trial (registration atclinicaltrials.gov NCT01900912), 2015. Available at http://www.cedlas-er.org/sites/default/files/cer_evaluation_files/mali-clts-impact-evaluation-2014.pdf.Accessed on 08 August 2018.Arnold 2009 (published)

Arnold B, Arana B, Mäusezahl D, Hubbard A, Colford Jr JM. Evaluation of apre-existing, 3-year household water treatment and handwashing interven-tion in rural Guatemala. International Journal of Epidemiology.2009;38(6):1651-61.Arnold 2018 (Correspondence)Arnold BF, Null C, Luby SP, Colford JM. Implications of WASH Benefits trialsfor water and sanitation. The lancet . Vol 6 June 2018. https://doi.org/10.1016/S2214-109X(18)30229-8Bowen 2012 (Published)Bowen A, Agboatwalla M, Luby S, Tobery T, Ayers T, Hoekstra R. Associationbetween intensive handwashing promotion and child development inKarachi, Pakistan: a cluster randomized controlled trial. Archives of pediatrics& adolescent medicine. 2012;166(11):1037-44.Clasen 2014 (Published)Clasen T, Boisson S, Routray P, Torondel B, Bell M, Cumming O, et al.Effectiveness of a rural sanitation programme on diarrhoea, soil-transmittedhelminth infection, and child malnutrition in Odisha, India: a cluster-randomised trial. The Lancet Global Health. 2014;2(11):e645-e53.Langford 2011 (Published)Langford R, Lunn P, Brick CP. Hand-washing, subclinical infections, andgrowth: A longitudinal evaluation of an intervention in Nepali slums. Ameri-can Journal of Human Biology. 2011;23(5):621-9.Marshak 2015 (Unpublished)Anastasia Marshak, Helen Young, and Anne Radday. Water, Livestock, andMalnutrition: Findings from an Impact Assessment of “Community Resilienceto Acute Malnutrition” Programming in the Dar Sila Region of Eastern Chad,2012–2015. Feinstein International Center, 2016. Available at https://www.alnap.org/help-library/water-livestock-and-malnutrition-findings-from-an-impact-assessment-of-%E2%80%9Ccommunity. Accessed on 07 August 2018.McGuigan 2011 (Published)McGuigan KG, Samaiyar P, du Preez M, Conroy RM. High compliancerandomized controlled field trial of solar disinfection of drinking water andits impact on childhood diarrhea in rural Cambodia. Environmental science &technology. 2011;45(18):7862-7.Muhoozi 2018 (Published)Muhoozi GK, Atukunda P, Diep LM, Mwadime R, Kaaya AN, Skaare AB, et al.Nutrition, hygiene, and stimulation education to improve growth, cognitive,language, and motor development among infants in Uganda: A cluster-randomized trial. Maternal & child nutrition. 2018;14(2):e12527.Patil 2014 (Published)Patil SR, Arnold BF, Salvatore AL, Briceno B, Ganguly S, Colford Jr JM, et al.The effect of India's total sanitation campaign on defecation behaviors andchild health in rural Madhya Pradesh: a cluster randomized controlled trial.PLoS medicine. 2014;11(8):e1001709.Pickering 2015 (Published)Pickering AJ, Djebbari H, Lopez C, Coulibaly M, Alzua ML. Effect of acommunity-led sanitation intervention on child diarrhoea and child growthin rural Mali: a cluster-randomised controlled trial. The Lancet Global Health.2015;3(11):e701-e11.Shafique 2013 (Unpublished)Shafique S. Prevention of linear growth faltering and reversal of stuntingamong low birth weight infants in rural Bangladesh: a 2x2 factorial clusterrandomized trial. PhD dissertation, 2013. Available at https://tspace.library.utoronto.ca/bitstream/1807/68990/1/Shafique_Sohana_201311_PhD_thesis.pdf.Accessed on 07 August 2018.

Authors’ contributionsBoth the authors actively participated during conception of the review,development of the protocol, review and synthesis of articles, and writingvarious parts of the report. ZG prepared the manuscript. Both the authorsread and approved the final manuscript.

FundingThe authors of this study didn’t receive funds from any funding institution.

Availability of data and materialsThe extracted data will be made available upon requesting the primaryauthor.

Ethics approval and consent to participateNot applicable for systematic reviews.


https://doi.org/10.1186/s13052-019-0666-2https://doi.org/10.1186/s13052-019-0666-2https://doi.org/10.1186/s13052-019-0666-2http://clinicaltrials.govhttp://www.cedlas-er.org/sites/default/files/cer_evaluation_files/mali-clts-impact-evaluation-2014.pdfhttp://www.cedlas-er.org/sites/default/files/cer_evaluation_files/mali-clts-impact-evaluation-2014.pdfhttps://doi.org/10.1016/S2214-109Xhttps://doi.org/10.1016/S2214-109Xhttps://www.alnap.org/help-library/water-livestock-and-malnutrition-findings-from-an-impact-assessment-of-%E2%80%9Ccommunityhttps://www.alnap.org/help-library/water-livestock-and-malnutrition-findings-from-an-impact-assessment-of-%E2%80%9Ccommunityhttps://www.alnap.org/help-library/water-livestock-and-malnutrition-findings-from-an-impact-assessment-of-%E2%80%9Ccommunityhttps://tspace.library.utoronto.ca/bitstream/1807/68990/1/Shafique_Sohana_201311_PhD_thesis.pdfhttps://tspace.library.utoronto.ca/bitstream/1807/68990/1/Shafique_Sohana_201311_PhD_thesis.pdf

Consent for publicationThis manuscript does not contain any individual person’s data.

Competing interestsThe authors declare that they have no competing interests.

Author details1Department of Environmental and Occupational Health and Safety, Instituteof Public Health, College of Medicine and Health Sciences, University ofGondar, Gondar, Ethiopia. 2Department of Preventive Medicine, School ofPublic Health, College of Health Sciences, Addis Ababa University, AddisAbaba, Ethiopia.

Received: 2 January 2019 Accepted: 17 June 2019

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AbstractBackgroundMethodsResultsConclusion

BackgroundMethodsResearch questionCriteria for considering studies for this reviewTypes of studiesTypes of participantsTypes of interventionsControlTypes of outcome measures

Search strategy and study selectionAssessment of methodological qualityData extractionAssessment of risk of bias in included studiesMeasures of treatment effectSensitivity analysisAssessment of heterogeneityAssessment of publication biasDealing with missing data

ResultsThe search processDescription and characteristics of included studiesRisk of bias in included studiesAllocationBlindingIncomplete outcome dataSelective reportingOther potential sources of biasEffects of WASH interventions on linear growthAssessment of heterogeneitySubgroup analysisSensitivity analysisPublication bias

DiscussionSummary of evidenceApplicability of evidenceLimitation

ConclusionAdditional filesAbbreviationsAcknowledgmentsAuthors’ contributionsFundingAvailability of data and materialsEthics approval and consent to participateConsent for publicationCompeting interestsAuthor detailsReferencesPublisher’s Note

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