Nutritional resilience in Nepal following the earthquake of 2015

The 2015 earthquake in Nepal caused massive damages and triggered relief activities to minimize human suffering. The post-earthquake nutrition and food security situation in the hardest hit areas remains uncertain. Two national cross-sectional surveys were conducted in 2014 and 2016 among households...

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Bibliographic Details
Published in:PloS one 2018-11, Vol.13 (11), p.e0205438-e0205438
Main Authors: Thorne-Lyman, Andrew L, K C, Angela, Manohar, Swetha, Shrestha, Binod, Nonyane, Bareng A S, Neupane, Sumanta, Bhandari, Shiva, Klemm, Rolf D, Webb, Patrick, West, Jr, Keith P
Format: Article
Language:English
Subjects:
Adolescent
Adult
Age
Biology and Life Sciences
Body height
Breast feeding
Child
Children
Children & youth
Clustering
Confidence intervals
Cross-Sectional Studies
Diet
Earthquake damage
Earthquakes
Earthquakes - history
Family Characteristics
Food
Food security
Food Supply
Hemoglobin
History, 21st Century
Households
Humans
Malnutrition
Mathematical analysis
Medicine and Health Sciences
Nepal
Nutrition
Nutritional Status
People and Places
Prevalence
Public health
Public Health Surveillance
Resilience
Robustness (mathematics)
Security
Seismic activity
Social Sciences
Socioeconomic Factors
Statistical analysis
Systematic review
Teams
Weight
Young Adult
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Summary:The 2015 earthquake in Nepal caused massive damages and triggered relief activities to minimize human suffering. The post-earthquake nutrition and food security situation in the hardest hit areas remains uncertain. Two national cross-sectional surveys were conducted in 2014 and 2016 among households (HH) with pre-school aged children or newly married women. Of the 21 village development committees (VDCs) included in this sample, 7 fell within "earthquake-affected" areas. This paper presents data from 982 HH, 1015 women, and 883 children from 2014 and 1056 HH, 1083 women, and 998 children from 2016 living in these areas, with longitudinal overlap of about 55%. Prevalence estimates and 95% confidence intervals were calculated, and logistic regression was used to calculate p-values, both using robust estimates of standard errors to account for clustering. From 2014 to 2016, child wasting (weight-for-height z score
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0205438