Evaluate the Effect of Planet Water Foundation's Clean Water, Sanitation and Hygiene Intervention

Assessing the Health Impact of Safe Water, Sanitation, and Hygiene Education in the Developing World

The objective of the study is to evaluate the impact of the Planet Water Foundation's intervention to improve Water, Sanitation and Hygiene related illness for reducing absenteeism and diarrheal episodes among school children resulting in improvement of their learning performance and ultimately long term economic prosperity.

Study Overview

• Status: Withdrawn
• Conditions: Diarrheal Episodes
• Intervention / Treatment: Behavioral: Planet Water Foundation's Program

Detailed Description

A multidimensional approach is needed to meet the Millennium development Goals (MDG) to address Water and Sanitation Hygiene Knowledge, Attitudes and Practices. The MDG of reducing child mortality by 2015 will require an increased focus on countries and regions where children death rates are the highest, and a renewed commitment to reach the most vulnerable children (UN, 2013). India and Nigeria account for more than one third of all deaths in children less than five years of age (UN, 2013). Using a combination of a research based literature review, expert opinion and the policy reports a multifactor model was developed to determine the necessary variables that need to be addressed so that an integrated approach to WASH related problems could be designed.

Variables were identified that impact WASH outcomes. These variables have been classified into several categories including:

1. Socio-demographics
2. Environmental and Access
3. Knowledge, Attitudes, and Practices
4. Family Health Profile
5. School Characteristics

The proposed study is to conduct a randomized control clinical trial to meet the overall study objectives by evaluating Planet Water Foundation's Program through the developed multidimensional framework. The proposed clinical trial will include 12 schools (6 residential and 6 non-residential). Six schools (n=3 residential; n=3 non-residential) each will be included into Group 1 (No Planet Water intervention) and Group 2 (With Planet Water intervention) from two locations including districts of Pune and Thane in the State of Maharashtra. Locations were matched based on similar water sources at school, toilet facilities, water quality testing, water purification techniques, geographic setting, and geographic population size.

Planet Water Foundation's Program (PWP) provides a school-based program with an aim to improve health outcomes in children using three components: (a) access to safe water, (b) access to hand washing facilities, and (c) access to water-health and hygiene education. As part of the PWP, access to safe water is provided through the use of an AquaTower, which incorporates an Ultra Filtration (UF) system. PWP's Water-Health & Hygiene education program is a four week program including four modules: (1) importance of clean water, (2) how to wash your hands, (3) when to wash your hands, and (4) protecting against germs through a hands-on, activity-based program that incorporates games, drama, song, and dance.

A mixed methods approach was used with open-ended and multiple-choice questions in the form of questionnaires. Questionnaires were translated from English into the local language (Marathi), and back-translated to English for analysis by BAIF Development Research Foundation in India. Students, household caregivers/parents, and teachers were given separate questionnaires. Three different interviews were conducted: (1) a household questionnaire conducted at the home of each student included in the study, (2) a teacher questionnaire administered at the schools, and (3) a student questionnaire administered with each student individually by the interviewer in a separate room away from other students. Group 1 and Group 2 were given the same questionnaires. All questionnaires were administered in cooperation with BAIF Development Research Foundation, a local foundation in India. The foundation received data collection and data entry training prior to the study. Data will be gathered on paper forms and all the data will be entered in Microsoft Excel®. The online data repository will be stored in an encrypted manner and will be password protected. The information will be stored behind a secure firewall provided by the local research foundation in India. All the data gathered on the paper forms will be stored in a locked cabinet, and information will be accessed to only those individuals who are directly involved in the research. Consent will be obtained ensuring that (1) only approved personnel are present during the consent process, (2) fewest number of individuals possible are aware of the subject's participation in the research, and (3) research activities are performed in as private of a place as possible.

The study sample will include six Group 1 (no PWP intervention) schools and six Group 2 (With PWP intervention) schools, with 60 students per school for a total of 360 students in Group 1 schools and 360 students in Group 2 schools. The sample size among the 12 schools was calculated assuming an intra-cluster correlation coefficient of p=0.01, this design will provide 80% power (Donner and Klar, 1996) to detect a medium effect size (Cohen 1992) of 0.50. The sample size will be increased by 15%, to 414 for intervention and control, to account for attrition.

Descriptive statistics will be computed for all variables to ensure data quality and to evaluate the assumptions of statistical tests. Variable distributions will be described with histograms, box plots, central tendencies, measures of variation and frequency distributions. We will test the assumption of normality for the outcome measures, within the PWP and control groups. Additionally, we will test the assumption that the PWP and control groups have equal variances. Generalized linear mixed models (GLMMs) with unequal variances (McCulloch et al., 2008) can be applied when the ideal conditions of normality and equal variances are not met. Testing for the equality of variances is available with the COVTEST statement, which is based on the ratio of residual likelihoods or pseudo-likelihoods. Residuals at each level of the model will be examined to assess model fit. Generalized linear mixed models using SAS PROC GLIMMIX also provides the flexibility to model different types of outcome variables (binary, count and continuous data). All analyses will be conducted using SAS/STAT® software for Windows version 9.2 or higher.

Prior to testing the study hypotheses, we will examine data for possible covariates. Potential confounding variables will be controlled for by entering these variables as covariates in the regression models. These explanatory variables will improve the detection of treatment effects by entering these variables as covariates in the GLMMs. These variables will be entered as a covariate if the univariate two-sided chi-square test comparing the PWP and control groups is significant at the alpha level of 0.20. In preliminary analysis, we will examine potential covariates using single-level however will we also examine potential covariates using hierarchical linear models. We will use a liberal alpha of 0.20 for the decision of whether to adjust for a covariate so that important confounding effects are not overlooked.

Hierarchical, correlated data structure: A hierarchical linear models (HLM) approach (Raudenbush and Bryk, 2002) will be used to account for the correlation induced by repeated measurements over time for students nested within schools. School will be modeled as a random effect. These HLMs will be implemented in SAS using generalized linear mixed models, which can be applied to analyze binary, count and continuous repeated measurement data, collected at unequal time intervals following a hierarchical structure (McCulloch and Searle, 2001; Liang and Zeger 1986). These models address the correlated nature of multiple measurements that come from each subject over time. We anticipate that generalized linear models will be appropriate for the data, but will also explore using the method of generalized estimating equations (GEE) (McCulloch and Searle, 2008). The GEE method does not require assumptions of normality and equal variances, but uses less information than linear mixed models since only the first two moments of the distribution are employed in GEE.

• Study Type: Interventional
• Phase: Not Applicable

Contacts and Locations

Study Locations

• India
  • Pune, India, 411058
    • BAIF Development Research Foundation

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 6 years to 12 years (Child)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• Children 6 to 12 years of age
• Caregivers among the of the participating children in non-residential schools
• Teachers of the children included in the study

Exclusion Criteria:

• Consent is not given

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Prevention
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Control; Control schools were included if they did not have any ongoing water and sanitation hygiene initiative.
Experimental: Planet Water Program Intervention; School were included that were receiving the Planet Water Foundation's Program (PWP) providing a school-based program focusing on three components: (a) access to safe water, (b) access to hand washing facilities, and (c) access to water-health and hygiene education. Children will have access to safe water in the schools provided through the use of an AquaTower, and a 4 week educational program.	Behavioral: Planet Water Foundation's Program; Planet Water Foundation's Program (PWP) provides a school-based program with an aim to improve health outcomes in children using three components: (a) access to safe water, (b) access to hand washing facilities, and (c) access to water-health and hygiene education. As part of the PWP, access to safe water is provided through the use of an AquaTower, which incorporates an Ultra Filtration (UF) system. WASH messaging is also posted around the AquaTower as derived from PWP's school-based four week Water-Health & Hygiene education program.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
School absence due to diarrhea episodes	Number of school days lost to diarrhea episodes	Month 4
Incidence of influenza or other respiratory illnesses	Occurrence of influenza or other respiratory illnesses	Month 4
Number diarrheal episodes	Total number of diarrhea episodes	Month 4

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change from baseline children's WASH knowledge, attitudes, and practices at 4 months	Child, Teacher and Household questionnaires to assess knowledge, attitudes and practices around water treatment, hygiene and sanitary practices	Baseline and Month 4
Average academic score	Overall academic scores	Year 1

Collaborators and Investigators

• Sponsor: University of Nebraska
• Investigators: Principal Investigator: Ashish Joshi, MD, PhD, MPH, University of Nebraska

Publications and helpful links

General Publications

• Cohen J. A power primer. Psychol Bull. 1992 Jul;112(1):155-9. doi: 10.1037//0033-2909.112.1.155.
• United Nations (UN). The Millennium Development Goals Report 2013. New York: United Nation, 2013.
• Hartup, WW. Peer relations. Handbook of child psychology 1983; 4:103-196.
• Kohlberg, L. Stage and sequence: The cognitive-developmental approach to socialization. New York: Rand McNally 1969: 347-480.
• Donner A, Klar N. Statistical considerations in the design and analysis of community intervention trials. J Clin Epidemiol. 1996 Apr;49(4):435-9. doi: 10.1016/0895-4356(95)00511-0.
• McCulloch, C. E., Searle, S. R., & Neuhaus, J. M.. Generalized, linear, and mixed models. New York: John Wiley & Sons 2008.
• Raudenbush, S. W. Bryk, A. S. Hierarchical linear models: Applications and data analysis methods. 2nd edition. Newbury Park, CA: Sage 2002.
• McCulloch, C. E., & Searle, S. R. Generalized, linear, and mixed models. New York: John Wiley & Sons 2001.
• Liang, K. Y., & Zeger, S. L. Longitudinal data analysis using generalized linear models. Biometrika 1986; 73:13-22.

Study record dates

Study Major Dates

• Study Start (Estimated): April 1, 2013
• Primary Completion (Actual): May 2, 2013
• Study Completion (Actual): May 2, 2013

Study Registration Dates

• First Submitted: December 9, 2013
• First Submitted That Met QC Criteria: December 18, 2013
• First Posted (Estimated): December 19, 2013

Study Record Updates

• Last Update Posted (Actual): August 14, 2023
• Last Update Submitted That Met QC Criteria: August 10, 2023
• Last Verified: August 1, 2023

More Information

Terms related to this study

• Keywords: Evaluation; Education; Water; Hygiene; Sanitation
• Other Study ID Numbers: 0266-13-EP