Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level Analysis

Interventions tackling multiple drivers of child malnutrition have potential, yet the evidence is limited and draws on different analysis and nutrition outcomes, reducing comparability. To better understand the advantages and disadvantages of three different analytical approaches on seven common nut...

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Main Authors: Anastasia Marshak, Helen Young, Anne Radday, Elena N. Naumova
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:https://www.mdpi.com/1660-4601/17/9/3121
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spelling doaj-17d3675fe44a472d808950d2b4ec03ac2020-11-25T02:06:22ZengMDPI AGInternational Journal of Environmental Research and Public Health1661-78271660-46012020-04-01173121312110.3390/ijerph17093121Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level AnalysisAnastasia Marshak0Helen Young1Anne Radday2Elena N. Naumova3Feinstein International Center, Tufts University, Boston, MA 02111, USAFeinstein International Center, Tufts University, Boston, MA 02111, USAFeinstein International Center, Tufts University, Boston, MA 02111, USAFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USAInterventions tackling multiple drivers of child malnutrition have potential, yet the evidence is limited and draws on different analysis and nutrition outcomes, reducing comparability. To better understand the advantages and disadvantages of three different analytical approaches on seven common nutrition indicators, we use panel data (2012, 2014, 2015) on 1420 households from a randomized control study of a multi-sectoral intervention in Chad. We compare program impact using three types of analysis: a cross-sectional analysis of non-matched children; a panel analysis on longitudinal outcomes following the worst-off child in the household; and a panel analysis on longitudinal outcomes of matched children. We find that the sensitivity of the nutrition outcomes to program impact increases with each subsequent analytical approach, despite the reduction in sample size, as the analysis is able to control for more non-measured child and household characteristics. In the matched child panel analysis, the odds of a child being severely wasted were 76% lower (CI: 0.59–0.86, <i>p</i> = 0.001), the odds of being underweight were 33% lower (CI: 0.15–0.48, <i>p</i> = 0.012), and weight-for-height z-score was 0.19 standard deviations higher (CI: 0.09–0.28, <i>p</i> = 0.022) in the treatment versus control group. The study provides evidence for multi-sectoral interventions to tackle acute malnutrition and recommends the best practice analytical approach.https://www.mdpi.com/1660-4601/17/9/3121nutritionearly childhoodmulti-sectoral programmingmixed-effects modelChadanalytical approach
collection DOAJ
language English
format Article
sources DOAJ
author Anastasia Marshak
Helen Young
Anne Radday
Elena N. Naumova
spellingShingle Anastasia Marshak
Helen Young
Anne Radday
Elena N. Naumova
Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level Analysis
International Journal of Environmental Research and Public Health
nutrition
early childhood
multi-sectoral programming
mixed-effects model
Chad
analytical approach
author_facet Anastasia Marshak
Helen Young
Anne Radday
Elena N. Naumova
author_sort Anastasia Marshak
title Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level Analysis
title_short Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level Analysis
title_full Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level Analysis
title_fullStr Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level Analysis
title_full_unstemmed Sensitivity of Nutrition Indicators to Measure the Impact of a Multi-Sectoral Intervention: Cross-Sectional, Household, and Individual Level Analysis
title_sort sensitivity of nutrition indicators to measure the impact of a multi-sectoral intervention: cross-sectional, household, and individual level analysis
publisher MDPI AG
series International Journal of Environmental Research and Public Health
issn 1661-7827
1660-4601
publishDate 2020-04-01
description Interventions tackling multiple drivers of child malnutrition have potential, yet the evidence is limited and draws on different analysis and nutrition outcomes, reducing comparability. To better understand the advantages and disadvantages of three different analytical approaches on seven common nutrition indicators, we use panel data (2012, 2014, 2015) on 1420 households from a randomized control study of a multi-sectoral intervention in Chad. We compare program impact using three types of analysis: a cross-sectional analysis of non-matched children; a panel analysis on longitudinal outcomes following the worst-off child in the household; and a panel analysis on longitudinal outcomes of matched children. We find that the sensitivity of the nutrition outcomes to program impact increases with each subsequent analytical approach, despite the reduction in sample size, as the analysis is able to control for more non-measured child and household characteristics. In the matched child panel analysis, the odds of a child being severely wasted were 76% lower (CI: 0.59–0.86, <i>p</i> = 0.001), the odds of being underweight were 33% lower (CI: 0.15–0.48, <i>p</i> = 0.012), and weight-for-height z-score was 0.19 standard deviations higher (CI: 0.09–0.28, <i>p</i> = 0.022) in the treatment versus control group. The study provides evidence for multi-sectoral interventions to tackle acute malnutrition and recommends the best practice analytical approach.
topic nutrition
early childhood
multi-sectoral programming
mixed-effects model
Chad
analytical approach
url https://www.mdpi.com/1660-4601/17/9/3121
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