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Balk et al.[23]
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Capture the effects of geographic and environmental variables on child hunger. Looking for causal relationships using micro-level data on a continental scale.
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- Spatial Statistics: Simple ordinary least squares (OLS) regression analysis
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- Agriculture
|
Local and regional data/Micro- and meso-level analysis
|
Retrospective (1995–2004)
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African, Asian and Latin American countries
|
|
- Climate
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|
- Health (+DHS*)
|
|
- Infrastructure
|
|
- Physiography
|
|
- Politics
|
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- Population
|
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Grace et al.[19]
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Evaluate the relationship between climate variables and child malnutrition using a food security framework
|
- Spatial Statistics: Multi-level linear regression model
|
- Climate
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Household and local data/Micro-level analysis
|
Retrospective and Prospective (1990–2039)
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Kenya
|
|
- DHS*
|
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- Livelihood
|
|
- Zones
|
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- Population
|
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- Spatial Interpolation: Geostatistical interpolation using a moving window regression
|
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Jankowska et al.[20]
|
Examine and project climate and health trends in the African Sahel through the spatial coupling of climate data and health data in Mali.
|
- Spatial Statistics: Multivariate linear regression analysis
|
- Climate
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Local and regional data/Meso-level analysis
|
Prospective (1960–2039)
|
Mali
|
|
- DHS*
|
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- Livelihood
|
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- Zones
|
|
- Physiography
|
|
- Population
|
|
- Spatial Interpolation: Geostatistical interpolation using a moving window regression
|
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Kandala et al.[25]
|
Investigate the geographical and socioeconomic determinants of childhood undernutrition. Explore regional patterns of undernutrition.
|
- Spatial Statistics: Bayesian geo-additive regression model based on Markov priors
|
- DHS*
|
Local data/Meso-level analysis
|
Retrospective (1992)
|
Malawi, Tanzania and Zambia
|
|
- Socioeconomics
|
|
Liu et al.[21]
|
Spatially explicit assessment of current and future hotspots of food insecurity in SSA. Analyzing the impact of climate change on crop production.
|
- Spatial Modeling: Simulate dynamics of agricultural production
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- Climate
|
Local and regional data/Meso-level analysis
|
Prospective (1990–2030)
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Sub-Saharan Africa
|
|
- DHS*
|
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- Economic
|
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- Population
|
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- Spatial Analysis: Hotspot analysis
|
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Margai[22]
|
Discuss the multi-dimensional causes of food insecurity conditions, analyze the relation- ships between food insufficiency and nutritional health outcomes among children, and identify the demographic, socio-economic and environmental correlates of these conditions.
|
- Spatial Analysis: Road network distance analysis
|
- Agriculture
|
Household and regional data/Meso-level analysis
|
Retrospective (1999)
|
Burkina Faso
|
|
- Spatial Interpolation: Kriging algorithm
|
- DHS*
|
|
- Spatial Statistics/Statistical Methods: Chi-square test, Logistic regression analysis
|
- Infrastructure
|
|
Pawloski et al.[26]
|
Examine geographic relationships of nutritional status, including underweight, overweight and obesity among Kenyan mothers and children.
|
- Spatial Statistics: Getis–Ord General G Statistics, Gi* Statistic
|
- DHS*
|
Local data/Meso-level analysis
|
Retrospective (2003/2006)
|
Kenya
|
|
Rowhani et al.[18]
|
Present the influence of the climate-induced changes of ecosystem resources on malnutrition and armed conflict.
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- Spatial Statistics: Logistic regression models
|
- Agriculture
|
Local and regional data/Micro- and meso-level analysis
|
Retrospective (1946–2006)
|
Sudan, Ethiopia and Somalia
|
|
- Economics
|
|
- Health
|
|
- Infrastructure
|
|
- Politics
|
|
Sherbinin[24]
|
Determine if, when controlling for income and the health conditions, biophysical and geographical variables help to explain variation in the rates of child malnutrition.
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- Spatial Statistics: OLS Regression, Spatial Autocorrelation, Spatial Error (SE) model
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- Agriculture
|
Regional data/Meso-level analysis
|
Retrospective
|
Africa
|
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- Climate
| | | |
| | | |
- Economics
| | | |
| | | |
- Health (+DHS*)
| | | |
| | | |
- Infrastructure
| | | |
| | | |
- Physiography
| | | |
| | | |
- Population
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