Effect of a low-cost, behaviour-change intervention on latrine use and safe disposal of child faeces in rural Odisha, India: a cluster-randomised controlled trial

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Summary

Background

Uptake of Government-promoted sanitation remains a challenge in India. We aimed to investigate a low-cost, theory-driven, behavioural intervention designed to increase latrine use and safe disposal of child faeces in India.

Methods

We did a cluster-randomised controlled trial between Jan 30, 2018, and Feb 18, 2019, in 66 rural villages in Puri, Odisha, India. Villages were eligible if not adjacent to another included village and not designated by the Government to be open-defecation free. All latrine-owning households in selected villages were eligible. We assigned 33 villages to the intervention via stratified randomisation. The intervention was required to meet a limit of US$20 per household and included a folk performance, transect walk, community meeting, recognition banners, community wall painting, mothers’ meetings, household visits, and latrine repairs. Control villages received no intervention. Neither participants nor field assessors were masked to study group assignment. We estimated intervention effects on reported latrine use and safe disposal of child faeces 4 months after completion of the intervention delivery using a difference-in-differences analysis and stratified results by sex. This study is registered at ClinicalTrials.gov, NCT03274245.

Findings

We enrolled 3723 households (1807 [48·5%] in the intervention group and 1916 [51·5%] in the control group). Analysis included 14 181 individuals (6921 [48·8%] in the intervention group and 7260 [51·2%] in the control group). We found an increase of 6·4 percentage points (95% CI 2·0–10·7) in latrine use and an increase of 15·2 percentage points (7·9–22·5) in safe disposal of child faeces. No adverse events were reported.

Interpretation

A low-cost behavioural intervention achieved modest increases in latrine use and marked increases in safe disposal of child faeces in the short term but was unlikely to reduce exposure to faecal pathogens to a level necessary to achieve health gains.

Funding

The Bill & Melinda Gates Foundation and International Initiative for Impact Evaluation.

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Most cited references 30

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The behaviour change wheel: A new method for characterising and designing behaviour change interventions

Susan Michie, Maartje M. van Stralen, Robert H. West (2012)

Background Improving the design and implementation of evidence-based practice depends on successful behaviour change interventions. This requires an appropriate method for characterising interventions and linking them to an analysis of the targeted behaviour. There exists a plethora of frameworks of behaviour change interventions, but it is not clear how well they serve this purpose. This paper evaluates these frameworks, and develops and evaluates a new framework aimed at overcoming their limitations. Methods A systematic search of electronic databases and consultation with behaviour change experts were used to identify frameworks of behaviour change interventions. These were evaluated according to three criteria: comprehensiveness, coherence, and a clear link to an overarching model of behaviour. A new framework was developed to meet these criteria. The reliability with which it could be applied was examined in two domains of behaviour change: tobacco control and obesity. Results Nineteen frameworks were identified covering nine intervention functions and seven policy categories that could enable those interventions. None of the frameworks reviewed covered the full range of intervention functions or policies, and only a minority met the criteria of coherence or linkage to a model of behaviour. At the centre of a proposed new framework is a 'behaviour system' involving three essential conditions: capability, opportunity, and motivation (what we term the 'COM-B system'). This forms the hub of a 'behaviour change wheel' (BCW) around which are positioned the nine intervention functions aimed at addressing deficits in one or more of these conditions; around this are placed seven categories of policy that could enable those interventions to occur. The BCW was used reliably to characterise interventions within the English Department of Health's 2010 tobacco control strategy and the National Institute of Health and Clinical Excellence's guidance on reducing obesity. Conclusions Interventions and policies to change behaviour can be usefully characterised by means of a BCW comprising: a 'behaviour system' at the hub, encircled by intervention functions and then by policy categories. Research is needed to establish how far the BCW can lead to more efficient design of effective interventions.

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Constructing socio-economic status indices: how to use principal components analysis.

S Vyas, L Kumaranayake (2006)

Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.

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To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.

Alan Hubbard, Jennifer Ahern, Nancy L Fleischer … (2010)

Two modeling approaches are commonly used to estimate the associations between neighborhood characteristics and individual-level health outcomes in multilevel studies (subjects within neighborhoods). Random effects models (or mixed models) use maximum likelihood estimation. Population average models typically use a generalized estimating equation (GEE) approach. These methods are used in place of basic regression approaches because the health of residents in the same neighborhood may be correlated, thus violating independence assumptions made by traditional regression procedures. This violation is particularly relevant to estimates of the variability of estimates. Though the literature appears to favor the mixed-model approach, little theoretical guidance has been offered to justify this choice. In this paper, we review the assumptions behind the estimates and inference provided by these 2 approaches. We propose a perspective that treats regression models for what they are in most circumstances: reasonable approximations of some true underlying relationship. We argue in general that mixed models involve unverifiable assumptions on the data-generating distribution, which lead to potentially misleading estimates and biased inference. We conclude that the estimation-equation approach of population average models provides a more useful approximation of the truth.

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Author and article information

Contributors

Bethany A Caruso

Journal

Journal ID (nlm-ta): Lancet Planet Health

Journal ID (iso-abbrev): Lancet Planet Health

Title: The Lancet. Planetary Health

Publisher: Elsevier B.V

ISSN (Electronic): 2542-5196

Publication date PMC-release: 09 February 2022

Publication date Collection: February 2022

Publication date (Electronic): 09 February 2022

Volume: 6

Issue: 2

Pages: e110-e121

Affiliations

[a ]Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

[b ]Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA

[c ]Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA

[d ]Independent Consultant, Bhubaneswar, India

[e ]College of Nursing, and College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA

[f ]Department of Disease Control, Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, UK

Author notes

[* ]Correspondence to: Dr Bethany Caruso, Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA bcaruso@ 123456emory.edu

Article

Publisher Item ID: S2542-5196(21)00324-7

DOI: 10.1016/S2542-5196(21)00324-7

PMC ID: 8850376

PubMed ID: 35150621

SO-VID: 0b41d1ec-11cd-40a3-9e7e-7c3f9655d607

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Effect of a low-cost, behaviour-change intervention on latrine use and safe disposal of child faeces in rural Odisha, India: a cluster-randomised controlled trial

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Clinical Social Work and Health Intervention

Most cited references 30

The behaviour change wheel: A new method for characterising and designing behaviour change interventions

Constructing socio-economic status indices: how to use principal components analysis.

To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.

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