sanitation hygiene infant nutrition efficacy (shine) trial ... · •anc •pmtct •immunization...

Sanitation Hygiene Infant Nutrition Efficacy (SHINE) Trial in Zimbabwe:

Jean Humphrey, ScDProfessor, Johns Hopkins Bloomberg School of Public HealthBaltimore MDDirector, Zvitambo Institute for Maternal and Child Health Research, Harare, Zimbabwe

Design and Methods

2 x 2 factorial design:independent and combined effects

ControlStandard of Care

WASHWater andSanitationHygiene

IYCFInfant and Young

Child Feeding

Study population:Women in Chirumanzi and Shurugwi districts who became pregnant

between November 2012 - March 2015

400 Village Health Workers (VHWs)employed by

Zimbabwe Ministry of Health and Child Care (MoHCC)

• Conducted prospective pregnancy surveillance

• Referred to SHINE

• 5280 women recruited

• Median (IQR) age at enrolment: 12.5 (9,16) wk gestation

43 Research nurses: Assessed outcomes at:

14, 32 wk gest & 1, 3, 6, 12, 18 moAssessed intervention uptake at 12 mo

400 VHWs Delivered treatment-arm-specific

behavior-change interventions at 15 infant age-specific visits

Interventions Outcome assessment

Interventions

All children received the Standard of Care (Control) interventions

• Exclusive breastfeeding intervention

• Promoted uptake:

• ANC

• PMTCT

• Immunization

• Family Planning

5 mo. 7 mo.

Module 1Into to IYCF

Keep breast-feeding!

Module 2Thick porridge

Nutributter

Module 3Process food

“A baby can eat anything adults

eat”

The IYCF Intervention

Module 4Feeding baby during illness

Nutributter delivered monthly

Module 5Feed your baby from each food

20-24 wkgest

The WASH Intervention

29 wk gest 4 mo.2 mo. 5 mo.Birth

5 Core Modules

20-24 wkgest

Module 1Put all feces in

latrine.Latrine

constructed

Use latrine

Centralized brick and

slab moulding

Community builders

MoHCCsupervised

2500 WASH latrines at enrolment

2500 Non-Wash latrines

after trial

20-24 wkgest

latrine.Latrine

constructedTippy Tapsinstalled

Use latrine

Module 2Handwashing with soap at key times

Soap delivered

29 wk gest

Soap delivered monthly

20-24 wkgest

latrine.Latrine

Use latrine

Soap delivery

29 wk gest

Module 3Protect

child from feces and soil

ingestionPlay space and mat delivered

2 mo.Birth

20-24 wkgest

latrine.Latrine

Use latrine

Soap delivery

29 wk gest

Module 3Protect

Module 4Treat drinking

water especially for infant after

EBFWater Guard

delivery

WaterGuarddelivered monthly

20-24 wkgest

latrine.Latrine

Use latrine

Soap delivery

29 wk gest

Module 3Protect

Module 4Treat drinking

water especially for infant after

EBFWater Guard

delivery

WaterGuarddelivered monthly

Module 5Prepare Hygienic complementary

Fidelity of Intervention Delivery

% WASH Households receiving commodities

0 20 40 60 80 100

≥ 80% chlorine

≥ 80% soap

Play yard

Baby mat

2 Tippy Taps

VIP latrine

WASH+IYCF WASH

% IYCF household receiving commodities

0 20 40 60 80 100

≥ 80% Nutributter

WASH+IYCF IYCF

Baseline 12 months

Open Defecation

SOC IYCF WASH WASH+IYCF

Baseline 12 months

Improved Latrine

Presence of improved latrine and Open Defecation by Household members at baseline and 12 months

Baseline 12 months

Handwashing station with soap and water

Baseline 12 months

Detectable Chlorine in Drinking Water

Presence of handwashing station with soap and water AND having detectable chlorine in drinking water at baseline and 12 months

WASH uptake: Infant faeces disposal and geophagia

12 months 12 months 12 months

Disposes nappy water in latrine

Child ever observed to eat chicken feces

Child ever observed to eat soil

IYCF uptake: Consumed Nutributter past 24 hours

100SOCIYCFWASHWASH+IYCF

% Children

SHINE outcomes

5280 pregnant women enrolled

138 (2.6%) Mothers lost363 (6.9%) fetal deaths, 4 mothers died+81 (1.5%) fetuses from twin/triplets840 HIV+ or unknown mothers

3989 live-born HIV unexposed infants191 (4.8%) infant deaths100 (2.5%) lost

3686 infants assessed at 18 months (97% live births surviving to 18 months)

No treatment group interaction

• WASH + IYCF did not have a greater effect on any outcome I will show you today than WASH alone or IYCF alone.

• Presenting collapsed arms – WASH vs non-WASH

• IYCF vs non-IYCF

2 x 2 factorial design:independent and combined effects

SOC/Control

Standard of Care

Child Feeding

IYCF arms

SOC/Control

Standard of Care

Child Feeding

IYCF arms

SOCStandard of Care

Child FeedingNon-IYCF IYCF

WASH arms

SOC/Control

Standard of Care

Child Feeding

WASH arms

SOCStandard of Care

Child Feeding

Non-WASH

Impact of Infant and Young Child Feeding (IYCF) Intervention

Effect of IYCF on LAZ at 18 months of age

Difference due to IYCF

NMean(SD)

Unadjusted(95%CI)

Adjusted(95%CI)

No IYCF

1792 -1.59 (1.08) +0.16 (0.08, 0.23)

p<0.001

+0.13(0.06, 0.20)

p<0.001IYCF 1879 -1.44 (1.06)

Effect of IYCF on Hemoglobin (g/dL) at 18 mth of age

Difference due to IYCF

NMean(SD)

Unadjusted(95%CI)

Adjusted(95%CI)

No IYCF

1759 11.63 (1.18) +0.20 (0.13, 0.28)

p<0.001

+0.19(0.12, 0.27)

P<0.001IYCF 1845 11.83 (1.15)

Effect of IYCF on Stunting and Anemia

% Stunted % Anemic

No IYCF IYCF

RR (95%CI)

Unadjusted0.79

(0.72, 0.87)

Adjusted0.81

(0.74, 0.88)

RR (95%CI)

Unadjusted0.75

(0.62, 0.90)

Adjusted0.76

(0.63, 0.92)

Impact of WASH intervention

Effect of WASH on LAZ at 18 months of age

Difference due to WASH

NMean(SD)

Unadjusted(95%CI)

Adjusted(95%CI)

No WASH 1769-1.52 (1.07) +0.02

(-0.06, 0.09)p=0.70

+0.05(-0.02, 0.12)

p=0.13WASH 1902-1.50 (1.07)

Difference due to WASH

NMean(SD)

Unadjusted(95%CI)

Adjusted(95%CI)

No WASH 174811.75 (1.13) -0.03

(-0.10, 0.05) p=0.47

-0.06(-0.14, 0.02)

p=0.13WASH 185611.72 (1.21)

Effect of WASH on Hemoglobin (g/dL) at 18 months of age

Effect of WASH on Stunting and Anemia

% Stunted % Anemic

No WASH WASH

RR (95%CI)

Unadjusted1.03

(0.93, 1.13)

Adjusted1.00

(0.91, 1.10)

RR (95%CI)

Unadjusted1.14

(0.95, 1.36)

Adjusted1.13

(0.92, 1.37)

Diarrhea

Main Effects

Prevalence(%)

Difference(95%CI)

pAdjusted(95%CI)

NO IYCF

9.9 1.0 (Ref) 1.0 (Ref)

IYCF 9.40.94

(0.77,1.16)0.82

0.97(0.80, 1.20)

NO WASH

8.4 Ref Ref

WASH 10.71.28

(1.04,1.57)0.02

1.15 (0.93, 1.41)

7 day diarrhea prevalence at 18 months

Impact of WASH on transmission of 40 enteropathogens at 12 and 18 months using TAC

Mean (SD) score

at 6 months

Mean (SD) score

at 12 months

Difference* in number

of pathogens at 6 &

12 months (95% CI)

Pathogen scores† WASH

Total pathogens 2.6 (1.51) 3.1 (1.46) -0.04 ( -0.21, 0.10)

Bacteria 2.1 (1.25) 2.1 (1.13) 0.05 ( -0.07, 0.16)

Viruses 0.3 (0.53) 0.3 (0.52) 0.00 ( -0.05, 0.05)

Parasites 0.3 (0.54) 0.7 (0.74) -0.09 ( -0.16, -0.03)

One new result

Rotavirus was the leading cause of diarrhea deaths among young children in GEMS study.

215,000 deaths per year globally

Oral vaccines are less efficacious in developing countries

98.0% RV efficacy in developed countries

39.3% RV efficacy in sub-Saharan Africa

Why the gap in vaccine efficacy?

*Environmental enteric dysfunction*Diarrhea*Subclinical gastrointestinal carriage of pathogens*Altered gut microbiota

These may all respond to WASHRotavirus vaccine introduced in Zimbabwe during SHINE

We investigated the impact of the SHINE WASH intervention on rotavirus vaccine efficacy

RVV dose 1

Rotavirus Vaccine performance in SHINE

6 weeks 10 weeks

RVV dose 2

Pre-vaccination RV IgA titre

Post-vaccination RV

IgA titre

SHINE 4 week visit

SHINE 12 week visit

Rotavirus vaccine (Rotarix) introduced in May 2014

Primary outcome: WASH increase RVV efficacy from 20% to 30%

Rotavirus seroconversion

Analysis Group n/N %

Absolute difference

(%) (95% CI)

Unadjusted AdjustedRelative

Risk (95% CI)

pRelative Risk

(95% CI)p

At least 1 dose vaccine

Non-WASH

43/219

19.610.6

(0.5, 20.7)0.03

1.00(ref)

1.00 (ref)

WASH33/109

30.31.48

(0.98, 2.24)0.06

(1.12, 2.42)0.01

2 doses vaccine

Non-WASH

41/190

21.613.7

(2.0, 25.4)0.02

1.0 (ref)

WASH30/85

35.31.56

(1.04, 2.34)0.03

(1.20, 2.45)0.00

The WASH Benefits (Bangladesh and Kenya)and SHINE Trials:

Interpretation of findings on linear growth and diarrhoea

Environmental and personal hygiene are centrally importantto child growth and other measures of child health

Current WASH interventions are not effective enough

We need to set WASH programming on a trajectory from business as usual toward Transformative WASH:

*Novel approaches to intensive behavior change communication*Innovative technology and tools

*Strengthened governance of systems that deliver these BCC and tools

WASH Benefits trials and SHINE:

• WASH interventions typical of rural large-scale WASH programs:• Pit latrine• Handwashing station• POU drinking water chlorination

• Promoted removal of animal and human faeces from the yard: • WASH Benefits trials: “Sani-Scoops“ and potties • SHINE: Clean play space

Together, the three trials:

• Studied >18,000 children

• Free latrines, soap, chlorine, Nutributter

• Delivered behaviour change modules based on years of

formative research and pilot testing and grounded in behaviour

change theory

• Implemented WASH to household; Infant Feeding to index child

• Measured outcomes by standardized and supervised research

The three trials: LAZ and Diarrhoea outcomes

• In all three trials:

• Infant feeding intervention significantly, but modestly increased linear growth by 0.13 – 0.25 LAZ

• WASH intervention had no effect on LAZ and integrating WASH with IYCF had no additional benefit on linear growth than implementation of IYCF alone

• Diarrhoea outcome:

• In Bangladesh all 6 intervention arms (except water treatment alone)

significantly reduced diarrhoea by 31-40%

• In Kenya and Zimbabwe none of the interventions reduced diarrhoea

4 questions:

1. Why did the WASH intervention fail to improve linear growth in all three trials?

2. Why did WASH reduce diarrhoea in Bangladesh but have no impact on diarrhoea in Kenya and Zimbabwe?

3. Would a CATS (Community Approaches to Total Sanitation) been more effective on stunting than the household level interventions implemented?

4. What recommendations can we offer based on all available evidence?

#1: Why did WASH fail to improve linear growth?

Throughout history, sweeping improvements in WASH through engineered WASH associated with improved growth

Brazil, Stunting declined 37% to 7% over 30 yearsEngineered WASH was strong statistically attributable factor

#1: Why did WASH fail to improve linear growth?

A very high level of hygiene may be required to support normal child growth;

We have evidence from the 3 trials of substantial faecal-oral transmission among children in the WASH arms despite our interventions

Dadirai Fundira, Jean Humphrey, Mduduzi N.N. Mbuya, Gretel Pelto, Larry Moulton, Naume V. Tavengwa, Rebecca J. Stoltzfus

Structured observations at 18 months of age:89 WASH, 90 Non-WASH

WASH mother’s were making visibly dirty objects less available to their children

Frequency of mouthing in 6 hours

All objects Visibly dirty objects

Non-WASH 229 102

WASH 228 85

Soil ingestion during 6 hour observation

WASH Non-WASH

% infants ingesting soil ≥ once 52% 70%

Frequency of ingesting, among those who ingested soil ≥ once

4.7 (4) 5.0 (4.4)

Mean soil ingestion (g) 9.8 g 10.5 g

Mean E coli ingestion (CFU) 952 1052

WASH Benefits Trials

• Kenya – 40% of mothers reported that their child ate soil in the previous week

• Bangladesh – testing of soil showed very high concentrations of faecal contamination; observations studies showed frequent hand and object mouthing

Interpretation

• Despite high uptake of WASH interventions, fecal exposure remained high

A very low exposure to fecal contamination may be required to support normal linear growth which can be achieved through engineered WASH interventions but was not achieved in our trials.

If this is true, why have so many observational studies showed strong associations between indicators of rural WASH (not engineered WASH) and growth?

Systematic review of publications from past 3 years:

89 observational studies investigated association of WASH with stunting

49 (55%) showed a significant association between a WASH indicator and growth

Most common: “access to improved sanitation”

We investigated this in the control groups of our trials

Country Change in LAZ 95% CI P value

Bangladesh + 0.22 (0.03 – 0.40) 0.02

Kenya + 0.15 (0.02 – 0.28) 0.02

Zimbabwe + 0.20 (0.08 – 0.31) < 0.001

Change in LAZ at 18-24 months associated with having improved sanitation at baseline (pregnancy)

In all 3 trials, households in the control arm that had access to improved sanitation during pregnancy had an infant who was 0.2 LAZ longer at 18 - 24 monthsYet, building improved latrines in these communities did not improve linear growth

Suggests:

•Observational estimates of WASH intervention impact may be particularly vulnerable to confounding;

•Use caution in interpreting as evidence for WASH policy and programming

• Triangulate with experimental findings

• WASH is essential for normal child growth.

• However, even when implemented rigorously, the elementary interventions delivered in our trials and commonly through rural WASH programmes, have too little impact on reducing faecal-oral transmission compared to the pervasive faecal contamination of living environments of the world’s poorest people.

#2 : Why did WASH reduce diarrhoea in Bangladesh but not Kenya or Zimbabwe?

Frequency of contact per month between behaviour change promoter and study participant

Kenya 1

Zimbabwe 1

Bangladesh 6

What studies are these statements based on?

“Handwashing with soap before meals and after toilet use has been shown to reduce diarrhoeal infections by 50%”

“Home-based chlorination of drinking water reduces diarrhoea by 25%”

#2 : Why did WASH reduce diarrhoea in Bangladesh but not Kenya or Zimbabwe?

#2 : Why did WASH reduce diarrhea in Bangladesh but not Kenya or Zimbabwe?

We conducted systematic reviews to examine the

characteristics of the studies that form the evidence bases that:

• Hand-washing promotion reduces diarrhea (19 studies)

• POU water treatment reduces diarrhea (30 studies)

Paid close attention to how often promoters contacted

households

Studies of hand-washing and diarrhea: frequency of contact between behavior change promoter and study participant

Follow up of behaviors after the trials ended

Pakistan hand-washing trial (53% reduction) • Returned 18 months later. • Followed same households for 14 months. • Soap purchasing not different between groups. • Child diarrhea not different between groups.

Guatemala flocculant disinfectant water treatment trial • Just after trial ended, social marketing of the solution rolled out in study

areas. • Six months later: 5% of intervention arm households had purchased

solution in past 2 weeks and used it in past 1 week.

• All the evidence that POU water treatment and hand-washing promotion reduce diarrhea comes from studies with very high intervention intensity (mostly daily to weekly contact between promoter and participant).

• Moreover, once these intense interventions stopped, the behaviors stopped, and the benefit on child diarrhea disappeared.

It is unlikely that POU Water treatment and hand-washing promotion implemented programmatically through social marketing, posters or pamphlets, or messages intermittently delivered by community health workers, unaccompanied by further investments in behavior change are reducing child diarrhea

Taken together:

#3 Would a CATS have been more effective?Children <2 spend most of their time in their household compound.Households are usually single-family dwellings surrounded by farm land. Mean distance between households was 82.6 m and population density was 18.6 people/Km2.

Perhaps visiting neighbors bring in contamination on their feet which further contaminates the soil children ingest. We think this is modest compared to the animals cohabitating in the household.

We did not chose CATS because introduces high variability in time to latrine and quality of latrine and current CATS programs seldom achieve universal uptake (10-50% OD continuing).

Effects from recent community sanitation studiesStudy Location Coverage Δ(%) Use (%) Exposure Diarrhoea Stunting

Arnold 2010 Tamil Nadu 1548 39

Patil 2014 Madhya Pradesh

2241 27

Clasen 2014 Orissa 863 46

Pickering 2015 Mali 3565 70 Note 1 +0.16 SD

WASH B-B B’desh 3595 (low OD)

>80 Note 2 Note 3

WASH B-K Kenya 1887(low OD)

Gram Vikas Orissa, India 1885 59 +0.17 SD

SHINE (preliminary) Zimbabwe High (?OD)

High ?

1. Fewer observed flies and feces; no change in fecal contamination of water2. Fewer observed soiled hands and less fecal contamination of water 3. Except water quality arm

#3 Would a CATS have been more effective?

One important exception was a CLTS trial conducted in Mali. The CLTS intervention has no effect on diarrhoea but increased LAZ by 0.18.

Reason 3 – Intervention duration/coverage

• Enrolled women in pregnancy• Aimed for families to change WASH behaviors before the baby

was born• Maybe it takes much longer to clean up heavily contaminated

environments and improve child health outcomes

• All 3 trials tested household-level interventions• Community latrine coverage may be an important factor• Improved LAZ seen in studies from India and Mali

Reese H, #170 ASTMH 2017; Pickering AJ, Lancet Glob Health 2015

Conclusions and recommendations for WASH policy and programming

Recommendations (1)

The WASH Benefits and SHINE trials provide high quality evidence that implementing the WASH interventions typically implemented in rural areas of LMICs is unlikely to increase child linear growth.

Implementing these interventions together with an infant feeding intervention is unlikely to increase child linear growth more than implementing the infant feeding intervention alone.

Policy and Programming should not promote these elementary WASH interventions alone or integrated with infant feeding interventions for the purpose of reducing stunting

Recommendations (2a)

• Virtually all the evidence that POU water treatment and handwashing promotion reduce diarrhoea comes from studies that had daily to fortnightly contact between promoters and households.

• When programme ended, behaviours steeply declined, and the effect on child diarrhoea disappeared.

• The dependence of these interventions on sustained daily to fortnightly behaviour change promotion may not be widely recognized by implementers.

Recommendations (2b)

• POU water treatment and hand-washing promotion through intermittent message delivery are unlikely to reduce child diarrhoea without further investment in behavior change, at least among children <2 years, who have the highest diarrhoea prevalence.

SHINE findings suggest that even small improvements in gut health might impact seroconversion to oral vaccines which are absorbed in the small intestine.

30% seroconversion is abysmal, but this improvement suggests that more transformative WASH may lead to greater gains.

Rotavirus Vaccine Efficacy

Set a trajectory toward “Transformative WASH”

1. Behavior change: More frequent/intense• Use of smart phone technology?

2. More effective technology which relies on less behavior change and reduces barriers to hygiene

3. Stronger governance, management, efficiency of human systems that deliver BCC and technology

Water Abundance Xprize awarded October 22, 2018: Skywater

*Harvests 2000 liters clean water from the atmosphere*Sustainable energy*<2 cents per liter

*One more tool to put on the buffet of possibilities for governments to choose from?*Sparsely populated areas where piping is infeasible?*Arid climates without ground water?*Push technology development faster?

Possible transformative WASH examples• BCC delivered through approaches that ensure frequent

message delivery or mass media that challenge social norms (parliament members washing hands)

• Complete separation of animal feces from children’s living environments

• Community coverage of sustained high quality sanitation• Continuous plentiful supply of an uncontaminated water

delivered into households (Skywater won Water Abundance Xprize on Oct 22 – 2000 liters/water/day from atmosphere using only renewable energy at a cost of <2 cents/liter

Donors

• Bill and Melinda Gates Foundation• Department for International

Development, UK (DFID)• Wellcome Trust

With additional support from:National Institutes of Health, USASwiss Agency for Development and CooperationEuropean Union, UNICEF

Collaborating Institutions and Investigators

Zimbabwe Ministry of Health and Child CareGoldberg Mangwadu, Ancikaria Chigumira, Cynthia Chasokela

Zvitambo Institute for Maternal and Child Health ResearchMduduzi Mbuya (currently GAIN), Robert Ntozini, Naume Tavengwa, Kuda Mutasa, Florence Majo, Bernard Chasekwa, Virginia Sauramba, Phillipa Rambanepasi

Johns Hopkins Bloomberg School of Public Health Jean Humphrey, Lawrence Moulton, Margaret Kosek

Queen Mary University of London Andrew Prendergast

Cornell University Rebecca Stoltzfus

University of Liverpool Melissa Gladstone

University of British Columbia Amee Manges

George Washington University James Tielsch

Middlebury College John Maluccio

University of Michigan Andrew Jones

The WASH Benefits trials in Kenya and Bangladesh:

Cluster-randomized controlled trials of water, sanitation, handwashing,

and nutritional interventions in rural settings

Presented by: Clair Null, Ph.D.Mathematica Policy Research and Innovations for Poverty Action

• Cluster-randomized

• Measure effects during the first two years of life

• Enroll pregnant women, follow children 12 & 24 months later

• 7 study arms

1. Double-sized control

2-5. Single interventions (W, S, H, N)

6-7. Combined interventions (WSH, WSHN)

One design, two trials

(Bangladesh: passive; Kenya: active)

Context

Bangladesh Kenya

Population

densityModerate Low

Water sourceShallow tubewell

(in compound)

Protected springs

(10 minute walk)

Sanitation

<60% own a latrine

Of which:

>90% concrete slab but

<33% functional water seal

>80% own a latrine

<20% access improved

Handwashing <10% have soap available at handwashing location

Food security ~30% food insecurity

~10% moderate to severe

household hunger

Target behaviors

Water Treat drinking water with chlorine.

Sanitation Use latrines for defecation and safely dispose of feces.

Handwashing Wash hands with soap before handling food and after defecation.

NutritionPractice UNICEF and Government of Kenya/Bangladesh

guidelines for maternal, infant, and young child feeding.

• Dietary diversity during pregnancy and lactation

• Early initiation of breastfeeding

• Exclusive breastfeeding until 6 months

• Introduction of appropriate and diverse complementary foods

at 6 months

• Continued breastfeeding through 24 months

• Nominated by study participants

• Trained and supported by study staff

• Ratio of 1:8 participants in Bangladesh; 1:~11 in Kenya

• Visits to educate, encourage behaviors, support hardware

• Kenya: monthly during year 1, ~6 weeks during year 2

• Bangladesh: 6 times / month throughout

Community promoters

Mean visits per

Handwashing

Nutrition

Sanitation

All Arms

Promoters:

flip charts

summary sheets

Participants:

calendars,

cue cards,

tracking booklets

Enrollment and loss to follow-up

Bangladesh Kenya

Baseline720 clusters,

5551 women

702 clusters,

8246 women

Follow up

(Year 2)

(93% of living children)

6583 children

(86% of living children)

Adherence

7-day diarrhea prevalence

Bangladesh

-2.00 -1.50 -1.00 -0.50 0.00

Nutrition + W+S+H

Nutrition

Handwashing

Sanitation

Control

p<0.001

p=0.029

Growth - Bangladesh

-2 -1.5 -1 -0.5 0

Combined WSH+N

Nutrition

Combined WSH

Handwashing

Sanitation

Passive Control

Active Control

Mean length for age Z score

(standard deviations)

2 year follow-up

Growth - Kenya

p=0.032

p=0.004

vs. control:

Anemia

Bangladesh Kenya

Child development

• Extended Ages and Stages Questionnaire

• Bangladesh: Effects on gross motor from N, WSH, WSHN;

effects on personal social from all intervention arms

• Kenya: No effects of any intervention on gross motor,

personal social, or communication

Bangladesh only:

• MacArthur Bates Communicative Development Inventory

• Effects from all arms on understanding and saying

• Almost no effects on tests of executive function

0% 1% 2% 3% 4% 5%

Nutrition + W+S+H

Nutrition

Handwashing

Sanitation

Control

Risk Ratio: 0.81; p=0.362

Risk Ratio: 0.62; p=0.037

Mortality - Bangladesh

0 1 2 3 4 5 6

Combined WSH+N

Nutrition

Combined WSH

Handwashing

Sanitation

Passive Control

Active Control

Percent of live births

Mortality - Kenya

Summary

Find links to all WASH Benefits publications at

http://www.washbenefits.net/publications.html

Summary• Same design and similar interventions, different contexts

• Much more promotion in Bangladesh (weekly versus ~6 weeks)

• Higher adherence in Bangladesh, but same growth results in

both countries (N and WSHN only)

• Very different diarrhea results• Bangladesh (very low prevalence - 6%): Impacts on diarrhea from all

interventions except W; direct evidence of reduction in Giardia

• Kenya (very high prevalence - >25%): No impacts on diarrhea or Giardia

• Significant reductions in anemia in both countries; suggestive

indications of WASH effects

• Strong effects on child development from all interventions in

Bangladesh, none in Kenya

• Statistically significant reduction in mortality in WSHN arm in

Bangladesh, similar trend in Kenya

Bangladesh AcknowledgementsICDDR,BAWESOME field teamLeanne UnicombMahbubur RahmanSania AshrafFaruqe HussainFosiul NizameShaila ArmanFarzana BegumAbu NaserSarker Masud ParvezFahmida TofailKishor DasSolaiman DozaRashidul HaqueTahmeed AhmedRubhana RaqibMahfuza Sheuli

In memoriamMothaher Hossain

StanfordAmy PickeringJessica GrembiLaura Kwong

UC DavisChristine StewartKay Dewey

Johns HopkinsPeter WinchElli Leontsini

UC BerkeleyJack ColfordBen ArnoldJade Benjamin-ChungLia FernaldAudrie LinAyse ErcumenPatricia Kariger

University at BuffaloPavani Ram

Emory UniversityTom Clasen

Kenya AcknowledgementsIPA

Geoffrey Nyambane

Theodora Meerkerk

Ryan Mahoney

Liz Jordan

Betty Akoth

Marion Kiprotich

Priscah Cheruiyot

Mathilda Regan

Jenna Swarthout

Stephen Kalungu

Frank Odhiambo

Ronald Omondi

Maryanne Mureithi

Beryl Achando

John Mboya

and the 200+ members of the intervention delivery, data collection, and laboratory teams

UC Berkeley

Jack Colford

Ben Arnold

Audrie Lin

Jade Benjamin-Chung

Andrew Mertens

Lia Fernald

Patricia Kariger

Alan Hubbard

Erin Milner

UC Davis

Christine Stewart

Holly Dentz

Kay Dewey

Charles Arnold

Kendra Byrd

Anne Williams

Stanford University

Steve Luby

Lauren Steinbaum

Tufts University

Amy Pickering

Sammy Njenga

Bernard Chieng

University at Buffalo

Pavani Ram

Emory University

Tom Clasen

Harvard University

Michael Kremerstudy promoters and participants

and the County Health Management Teams for their support

Funding: This research was financially supported by Grant OPPGD759 from the Bill & Melinda Gates

Foundation to the University of California, Berkeley and the generosity of the American people through the

United States Agency for International Development (USAID). The contents of this presentation are the

responsibility of the authors and do not necessarily reflect the views of USAID or the United States

Government.

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