BACKGROUND: Social impact interventions often involve the introduction of a product intended to create positive impact. Program decision makers need data to routinely review product delivery as well as predict potential outcomes and impact to optimize intervention plans and allocate resources effectively. We propose a novel model to support data-driven decision-making in data and budget-constrained settings and use of routine monitoring to ensure progress towards program outcomes and impact. METHODS: We present a complete model to estimate product reach of durable and fast-moving consumer products, which includes required inputs, potential data sources, formulas, trade-offs, and assumptions. RESULTS: We illustrate the use of the model by applying it to the case study of fortified rice introduction in Brazil and estimate that the intervention, which aimed to improve nutrition status and health outcomes reached 2.4 million consumers. CONCLUSIONS: The model can cover a broad range of social-purpose interventions that involve the introduction or scale-up of various types of consumer products. It provides a relatively simple, comprehensive, flexible, and usable framework to estimate product reach, an indicator that can be an input into impact estimates or, in many scenarios, the actual endpoint of the intervention.
BACKGROUND: Social impact interventions often involve the introduction of a product intended to create positive impact. Program decision makers need data to routinely review product delivery as well as predict potential outcomes and impact to optimize intervention plans and allocate resources effectively. We propose a novel model to support data-driven decision-making in data and budget-constrained settings and use of routine monitoring to ensure progress towards program outcomes and impact. METHODS: We present a complete model to estimate product reach of durable and fast-moving consumer products, which includes required inputs, potential data sources, formulas, trade-offs, and assumptions. RESULTS: We illustrate the use of the model by applying it to the case study of fortified rice introduction in Brazil and estimate that the intervention, which aimed to improve nutrition status and health outcomes reached 2.4 million consumers. CONCLUSIONS: The model can cover a broad range of social-purpose interventions that involve the introduction or scale-up of various types of consumer products. It provides a relatively simple, comprehensive, flexible, and usable framework to estimate product reach, an indicator that can be an input into impact estimates or, in many scenarios, the actual endpoint of the intervention.
Authors: Robert E Black; Lindsay H Allen; Zulfiqar A Bhutta; Laura E Caulfield; Mercedes de Onis; Majid Ezzati; Colin Mathers; Juan Rivera Journal: Lancet Date: 2008-01-19 Impact factor: 79.321
Authors: Jennifer Coates; Brooke Colaiezzi; John L Fiedler; James Wirth; Keith Lividini; Beatrice Rogers Journal: Food Nutr Bull Date: 2012-09 Impact factor: 2.069
Authors: Anthony Burton; Roeland Monasch; Barbara Lautenbach; Marta Gacic-Dobo; Maryanne Neill; Rouslan Karimov; Lara Wolfson; Gareth Jones; Maureen Birmingham Journal: Bull World Health Organ Date: 2009-07 Impact factor: 9.408
Authors: Carmen Forsman; Peiman Milani; Jill A Schondebare; Dipika Matthias; Christophe Guyondet Journal: Ann N Y Acad Sci Date: 2014-06-09 Impact factor: 5.691
Authors: Grant J Aaron; Valerie M Friesen; Svenja Jungjohann; Greg S Garrett; Lynnette M Neufeld; Mark Myatt Journal: J Nutr Date: 2017-04-12 Impact factor: 4.798
Authors: Marie Ng; Nancy Fullman; Joseph L Dieleman; Abraham D Flaxman; Christopher J L Murray; Stephen S Lim Journal: PLoS Med Date: 2014-09-22 Impact factor: 11.069