Shared geographic spatial risk of childhood undernutrition in Malawi: An application of joint spatial component model
1Basic Sciences Department, Lilongwe University of Agriculture and Natural Resources, P.O Box 219, Lilongwe, Malawi.
Insights
Childhood undernutrition in Malawi shows significant spatial clustering, particularly for stunting and wasting, and wasting and underweight. Interventions should target southern and central regions.
Area of Science:
- Public Health
- Spatial Epidemiology
- Child Nutrition
Background:
- Child undernutrition remains a significant public health concern in Malawi.
- Understanding the spatial distribution of undernutrition indicators is crucial for targeted interventions.
Purpose of the Study:
- To assess the shared spatial risk of childhood undernutrition indicators (stunting, wasting, underweight) in Malawi.
- To identify geographical areas with a higher co-occurrence of these indicators.
Main Methods:
- A cross-sectional study using data from the 2015/16 Malawi Demographic and Health Survey (5066 child records).
- A shared spatial component model was applied to stunting, wasting, and underweight.
- Districts were modeled as spatial components using a convolution prior and conditional autoregressive distribution.
Main Results:
- Significant spatial clustering was found for stunting and wasting (Moran I = 0.464, p=0.009) and wasting and underweight (Moran I = 0.392, p=0.026).
- Risk maps indicated higher joint risks in southern and central districts compared to northern districts.
- Shared spatial risk for stunting and underweight was randomly dispersed (Moran I = -0.044, p=0.539).
Conclusions:
- Interventions to reduce child undernutrition should prioritize southern and central regions of Malawi.
- Addressing overpopulation and climate change impacts may be necessary to mitigate undernutrition risks.
Objectives:
This study aimed at assessing shared spatial risk of childhood undernutrition indicators in Malawi.
Study Design:
Cross-sectional design.
Methods:
The shared spatial component model was fitted to childhood undernutrition indicators, namely: stunting, wasting and underweight, using 5066 child records of the 2015/16 Malawi demographic health survey data. The spatial components were districts, and were modeled by the convolution prior, with the structured components being assigned the conditional autoregressive distribution.
Results:
There is significant clustering of shared spatial risk of stunting and wasting (Moran I = 0.464, p-value = 0.009), and wasting and underweight (Moran I = 0.392, p-value = 0.026), and the risk maps show southern districts, followed by central districts being at greater risk of jointly having stunting and wasting, wasting and underweight, compared to the northern region districts. The shared spatial risk of stunting and underweight is randomly dispersed across the country (Moran I = - 0.044, p-value = 0.539).
Conclusion:
Interventions to reduce the shared risk of child undernutrition should focus on the southern region districts and those in the central region, and a suggestion is made to address the issue of overpopulation and effects of climate change.
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