Evidence map›Paper›PMID 31533762›Full record

ArticleBMC public health2019

A behavioral design approach to improving a Chagas disease vector control campaign in Peru.

Alison M Buttenheim, Michael Z Levy, Ricardo Castillo-Neyra, Molly McGuire, Amparo M Toledo Vizcarra, Lina M Mollesaca Riveros, Julio Meza, Katty Borrini-Mayori, Cesar Naquira, Jere Behrman and 1 more

Abstract read
In one paragraph

Article in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Alison M ButtenheimDepartment of Family and Community Health, University of Pennsylvania School of Nursing, 418 Curie Boulevard, 416 Fagin Hall, Philadelphia, PA, 19104, USA. abutt@nursing.upenn.edu.ORCID http://orcid.org/0000-0003-2432-1637
Michael Z LevyDepartment of Biostatistics, Epidemiology, and Informatics, Perleman School of Medicine of the University of Pennsylvana, Philadelphia, PA, USA.
Ricardo Castillo-NeyraDepartment of Biostatistics, Epidemiology, and Informatics, Perleman School of Medicine of the University of Pennsylvana, Philadelphia, PA, USA.
Molly McGuireDepartment of Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.
Amparo M Toledo VizcarraFacultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru.
Lina M Mollesaca RiverosFacultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru.
Julio MezaFacultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru.
Katty Borrini-MayoriFacultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru.
Cesar NaquiraFacultad de Salud Pública y Administración, Universidad Peruana Cayetano Heredia, Lima, Peru.
Jere BehrmanDepartment of Economics, University of Pennsylvania, Philadelphia, PA, USA.
Valerie A Paz-SoldanDepartment of Global Community Health and Behavioral Sciences, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA.

Funding

Improving participation in vector control campaigns R01HD075869 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI BUTTENHEIM, ALISON MEREDITH, LEVY, MICHAEL Z · 2014 to 2018
$2.6M
Eunice Kennedy Shriver National Institute of Child Health and Human Development R01-HD075869NICHD NIH HHS R01 HD075869
6 · The paper itself

Abstract

backgroundIndividual behavior change is a critical ingredient in efforts to improve global health. Central to the focus on behavior has been a growing understanding of how the human brain makes decisions, from motivations and mindsets to unconscious biases and cognitive shortcuts. Recent work in the field of behavioral economics and related fields has contributed to a rich menu of insights and principles that can be engineered into global health programs to increase impact and reach. However, there is little research on the process of designing and testing interventions informed by behavioral insights.

methodsIn a study focused on increasing household participation in a Chagas disease vector control campaign in Arequipa, Peru, we applied Datta and Mullainathan's "behavioral design" approach to formulate and test specific interventions. In this Technical Advance article we describe the behavioral design approach in detail, including the Define, Diagnosis, Design, and Test phases. We also show how the interventions designed through the behavioral design process were adapted for a pragmatic randomized controlled field trial.

resultsThe behavioral design framework provided a systematic methodology for defining the behavior of interest, diagnosing reasons for household reluctance or refusal to participate, designing interventions to address actionable bottlenecks, and then testing those interventions in a rigorous counterfactual context. Behavioral design offered us a broader range of strategies and approaches than are typically used in vector control campaigns.

conclusionsCareful attention to how behavioral design may affect internal and external validity of evaluations and the scalability of interventions is needed going forward. We recommend behavioral design as a useful complement to other intervention design and evaluation approaches in global health programs.

Indexed as

Disease VectorsHealth BehaviorAnimalsChagas DiseaseGlobal HealthHealth Services AccessibilityHumansPeruRandomized Controlled Trials as TopicResearch DesignBehavioral economicsChagas diseaseIntervention designPeruPublic healthVector control

Identifiers

PMID31533762
PMCPMC6751594

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.