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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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 |
work_keys_str_mv |
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