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Social Vulnerability and Ebola Virus Disease in Rural Liberia.

Stanturf JA, Goodrick SL, Warren ML, Charnley S, Stegall CM - PLoS ONE (2015)

Bottom Line: Our results illustrate how census and household survey data, when displayed spatially at a sub-county level, may help highlight the location of the most vulnerable households and populations.Our results can be used to identify vulnerability hotspots where development strategies and allocation of resources to address the underlying causes of vulnerability in Liberia may be warranted.We demonstrate how social vulnerability index approaches can be applied in the context of disease outbreaks, and our methods are relevant elsewhere.

View Article: PubMed Central - PubMed

Affiliation: Center for Forest Disturbance Science, U.S. Forest Service, Athens, Georgia, United States of America.

ABSTRACT
The Ebola virus disease (EVD) epidemic that has stricken thousands of people in the three West African countries of Liberia, Sierra Leone, and Guinea highlights the lack of adaptive capacity in post-conflict countries. The scarcity of health services in particular renders these populations vulnerable to multiple interacting stressors including food insecurity, climate change, and the cascading effects of disease epidemics such as EVD. However, the spatial distribution of vulnerable rural populations and the individual stressors contributing to their vulnerability are unknown. We developed a Social Vulnerability Classification using census indicators and mapped it at the district scale for Liberia. According to the Classification, we estimate that districts having the highest social vulnerability lie in the north and west of Liberia in Lofa, Bong, Grand Cape Mount, and Bomi Counties. Three of these counties together with the capital Monrovia and surrounding Montserrado and Margibi counties experienced the highest levels of EVD infections in Liberia. Vulnerability has multiple dimensions and a classification developed from multiple variables provides a more holistic view of vulnerability than single indicators such as food insecurity or scarcity of health care facilities. Few rural Liberians are food secure and many cannot reach a medical clinic in <80 minutes. Our results illustrate how census and household survey data, when displayed spatially at a sub-county level, may help highlight the location of the most vulnerable households and populations. Our results can be used to identify vulnerability hotspots where development strategies and allocation of resources to address the underlying causes of vulnerability in Liberia may be warranted. We demonstrate how social vulnerability index approaches can be applied in the context of disease outbreaks, and our methods are relevant elsewhere.

No MeSH data available.


Related in: MedlinePlus

Distributions of district scores on seven factors.Distribution of social vulnerability scores from factor analysis for districts classified into five clusters (using NbClust) allowing evaluation of the influence of each respective social vulnerability factor on each cluster. For each cluster of districts, vertical lines indicate the mean (central cross bar) and maximum and minimum factor scores and boxes delineate quartile factor scores across all seven factors in each cluster of districts. Factor 1- Water Quality/Medical Proximity; Factor 2- Food Quality; Factor 3- Food Quantity; Factor 4- Displaced Populations; Factor 5 –Disabled and Dependent Populations; Factor 6 –Access to Land and Free Medical Care; Factor 7- Lack of Material Goods.
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pone.0137208.g001: Distributions of district scores on seven factors.Distribution of social vulnerability scores from factor analysis for districts classified into five clusters (using NbClust) allowing evaluation of the influence of each respective social vulnerability factor on each cluster. For each cluster of districts, vertical lines indicate the mean (central cross bar) and maximum and minimum factor scores and boxes delineate quartile factor scores across all seven factors in each cluster of districts. Factor 1- Water Quality/Medical Proximity; Factor 2- Food Quality; Factor 3- Food Quantity; Factor 4- Displaced Populations; Factor 5 –Disabled and Dependent Populations; Factor 6 –Access to Land and Free Medical Care; Factor 7- Lack of Material Goods.

Mentions: Exploratory factor analysis reduced our 18 variables (Table 2) to seven interpretable factors that explained 64% of the variance (Fig 1). The variance explained for the factor analysis is lower than the 77% produced by principal components analysis [38]; however, in the principal component analysis variables loaded onto multiple components, which reduces the explanatory power of the components. In the factor analysis, most variables loaded onto just one factor. Factor 1 is a “water quality-proximity to medical care” factor; Factor 2 relates to “food quality” and Factor 3 to “food quantity.” Factor 4 reflects the added stress on communities as “displaced populations,” and Factor 5 groups “disabled and dependent populations”. Factors 6 and 7 were not easily interpretable, but Factor 6 couples lack of access to free medical care and land; Factor 7 is most influenced by lack of material goods (furniture or mattresses). Loadings for most variables were associated with a single factor except for percentage of households lacking a mattress, which was associated with both displaced populations (Factor 4) and lack of material goods (Factor 7). We interpret this as instances of specific and general poverty. Factor 3 (food quantity) has an apparently contradictory positive correlation with undernourishment and negative correlation with stunted children. We speculate that in households that lack sufficient food, children do not survive to become stunted.


Social Vulnerability and Ebola Virus Disease in Rural Liberia.

Stanturf JA, Goodrick SL, Warren ML, Charnley S, Stegall CM - PLoS ONE (2015)

Distributions of district scores on seven factors.Distribution of social vulnerability scores from factor analysis for districts classified into five clusters (using NbClust) allowing evaluation of the influence of each respective social vulnerability factor on each cluster. For each cluster of districts, vertical lines indicate the mean (central cross bar) and maximum and minimum factor scores and boxes delineate quartile factor scores across all seven factors in each cluster of districts. Factor 1- Water Quality/Medical Proximity; Factor 2- Food Quality; Factor 3- Food Quantity; Factor 4- Displaced Populations; Factor 5 –Disabled and Dependent Populations; Factor 6 –Access to Land and Free Medical Care; Factor 7- Lack of Material Goods.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556488&req=5

pone.0137208.g001: Distributions of district scores on seven factors.Distribution of social vulnerability scores from factor analysis for districts classified into five clusters (using NbClust) allowing evaluation of the influence of each respective social vulnerability factor on each cluster. For each cluster of districts, vertical lines indicate the mean (central cross bar) and maximum and minimum factor scores and boxes delineate quartile factor scores across all seven factors in each cluster of districts. Factor 1- Water Quality/Medical Proximity; Factor 2- Food Quality; Factor 3- Food Quantity; Factor 4- Displaced Populations; Factor 5 –Disabled and Dependent Populations; Factor 6 –Access to Land and Free Medical Care; Factor 7- Lack of Material Goods.
Mentions: Exploratory factor analysis reduced our 18 variables (Table 2) to seven interpretable factors that explained 64% of the variance (Fig 1). The variance explained for the factor analysis is lower than the 77% produced by principal components analysis [38]; however, in the principal component analysis variables loaded onto multiple components, which reduces the explanatory power of the components. In the factor analysis, most variables loaded onto just one factor. Factor 1 is a “water quality-proximity to medical care” factor; Factor 2 relates to “food quality” and Factor 3 to “food quantity.” Factor 4 reflects the added stress on communities as “displaced populations,” and Factor 5 groups “disabled and dependent populations”. Factors 6 and 7 were not easily interpretable, but Factor 6 couples lack of access to free medical care and land; Factor 7 is most influenced by lack of material goods (furniture or mattresses). Loadings for most variables were associated with a single factor except for percentage of households lacking a mattress, which was associated with both displaced populations (Factor 4) and lack of material goods (Factor 7). We interpret this as instances of specific and general poverty. Factor 3 (food quantity) has an apparently contradictory positive correlation with undernourishment and negative correlation with stunted children. We speculate that in households that lack sufficient food, children do not survive to become stunted.

Bottom Line: Our results illustrate how census and household survey data, when displayed spatially at a sub-county level, may help highlight the location of the most vulnerable households and populations.Our results can be used to identify vulnerability hotspots where development strategies and allocation of resources to address the underlying causes of vulnerability in Liberia may be warranted.We demonstrate how social vulnerability index approaches can be applied in the context of disease outbreaks, and our methods are relevant elsewhere.

View Article: PubMed Central - PubMed

Affiliation: Center for Forest Disturbance Science, U.S. Forest Service, Athens, Georgia, United States of America.

ABSTRACT
The Ebola virus disease (EVD) epidemic that has stricken thousands of people in the three West African countries of Liberia, Sierra Leone, and Guinea highlights the lack of adaptive capacity in post-conflict countries. The scarcity of health services in particular renders these populations vulnerable to multiple interacting stressors including food insecurity, climate change, and the cascading effects of disease epidemics such as EVD. However, the spatial distribution of vulnerable rural populations and the individual stressors contributing to their vulnerability are unknown. We developed a Social Vulnerability Classification using census indicators and mapped it at the district scale for Liberia. According to the Classification, we estimate that districts having the highest social vulnerability lie in the north and west of Liberia in Lofa, Bong, Grand Cape Mount, and Bomi Counties. Three of these counties together with the capital Monrovia and surrounding Montserrado and Margibi counties experienced the highest levels of EVD infections in Liberia. Vulnerability has multiple dimensions and a classification developed from multiple variables provides a more holistic view of vulnerability than single indicators such as food insecurity or scarcity of health care facilities. Few rural Liberians are food secure and many cannot reach a medical clinic in <80 minutes. Our results illustrate how census and household survey data, when displayed spatially at a sub-county level, may help highlight the location of the most vulnerable households and populations. Our results can be used to identify vulnerability hotspots where development strategies and allocation of resources to address the underlying causes of vulnerability in Liberia may be warranted. We demonstrate how social vulnerability index approaches can be applied in the context of disease outbreaks, and our methods are relevant elsewhere.

No MeSH data available.


Related in: MedlinePlus