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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

Clusters of social vulnerability in rural Liberia, by district.Based on strength and distribution of factor scores (see Fig 1), social vulnerability of each cluster of districts can be loosely ranked from most to least vulnerable as: Cluster 1, food quality, displaced persons, disabled, dependent populations; Cluster 3, food quantity, food quality, lack of access to land/free medical care; Cluster 4, food quantity, disabled dependent populations and Cluster 5, water quality/proximity to medical care; and finally, Cluster 2, no strong vulnerability scores (county boundaries are in black, district boundaries in gray, main roads in yellow).
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pone.0137208.g002: Clusters of social vulnerability in rural Liberia, by district.Based on strength and distribution of factor scores (see Fig 1), social vulnerability of each cluster of districts can be loosely ranked from most to least vulnerable as: Cluster 1, food quality, displaced persons, disabled, dependent populations; Cluster 3, food quantity, food quality, lack of access to land/free medical care; Cluster 4, food quantity, disabled dependent populations and Cluster 5, water quality/proximity to medical care; and finally, Cluster 2, no strong vulnerability scores (county boundaries are in black, district boundaries in gray, main roads in yellow).

Mentions: Cluster analysis allowed classification of the districts based on their social vulnerability scores for each of the seven factors (Fig 2); some counties had districts in different clusters. Cluster 1 (Lofa, Bong, Grand Cape Mount, and Bomi Counties) showed the most overall vulnerability because it had the most positive scores among the seven factors with positive values for Factors 2 (food quality), 4 (displaced persons) and 5 (disabled and dependent populations). Cluster 3 (Montserrado and Grand Kru) was the next most vulnerable cluster, based on positive scores for food quantity (Factor 3), food quality, and lack of access to land/free medical care (Factor 6). Clusters 4 and 5 were somewhat vulnerable groups with differing concerns dominating. Cluster 4 (districts of River Gee County and northern Maryland County) had positive scores for food quantity (Factor 3) and disabled/dependent populations (Factor 5) with negative values for other factors. Water quality/proximity to medical facilities (Factor 1) was a concern for Cluster 5 (districts in Grand Bassa, River Cess, most of Sinoe and Gbarpolu, and portions of Margibi, Nimba, and Grand Gedeh Counties) and negative scores for food quantity (Factor 3), disabled and dependent populations (Factor 5), and access to land/free medical care (Factor 6). Cluster 2 (Nimba, Margibi, Grand Gedeh, some of Gbarpolu, and a few districts in Grand Cape Mount, Montserrado, Sinoe, and Maryland Counties) was the least vulnerable cluster with negative values for water quality/proximity to medical facilities (Factor 1) and food quantity (Factor 3).


Social Vulnerability and Ebola Virus Disease in Rural Liberia.

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

Clusters of social vulnerability in rural Liberia, by district.Based on strength and distribution of factor scores (see Fig 1), social vulnerability of each cluster of districts can be loosely ranked from most to least vulnerable as: Cluster 1, food quality, displaced persons, disabled, dependent populations; Cluster 3, food quantity, food quality, lack of access to land/free medical care; Cluster 4, food quantity, disabled dependent populations and Cluster 5, water quality/proximity to medical care; and finally, Cluster 2, no strong vulnerability scores (county boundaries are in black, district boundaries in gray, main roads in yellow).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0137208.g002: Clusters of social vulnerability in rural Liberia, by district.Based on strength and distribution of factor scores (see Fig 1), social vulnerability of each cluster of districts can be loosely ranked from most to least vulnerable as: Cluster 1, food quality, displaced persons, disabled, dependent populations; Cluster 3, food quantity, food quality, lack of access to land/free medical care; Cluster 4, food quantity, disabled dependent populations and Cluster 5, water quality/proximity to medical care; and finally, Cluster 2, no strong vulnerability scores (county boundaries are in black, district boundaries in gray, main roads in yellow).
Mentions: Cluster analysis allowed classification of the districts based on their social vulnerability scores for each of the seven factors (Fig 2); some counties had districts in different clusters. Cluster 1 (Lofa, Bong, Grand Cape Mount, and Bomi Counties) showed the most overall vulnerability because it had the most positive scores among the seven factors with positive values for Factors 2 (food quality), 4 (displaced persons) and 5 (disabled and dependent populations). Cluster 3 (Montserrado and Grand Kru) was the next most vulnerable cluster, based on positive scores for food quantity (Factor 3), food quality, and lack of access to land/free medical care (Factor 6). Clusters 4 and 5 were somewhat vulnerable groups with differing concerns dominating. Cluster 4 (districts of River Gee County and northern Maryland County) had positive scores for food quantity (Factor 3) and disabled/dependent populations (Factor 5) with negative values for other factors. Water quality/proximity to medical facilities (Factor 1) was a concern for Cluster 5 (districts in Grand Bassa, River Cess, most of Sinoe and Gbarpolu, and portions of Margibi, Nimba, and Grand Gedeh Counties) and negative scores for food quantity (Factor 3), disabled and dependent populations (Factor 5), and access to land/free medical care (Factor 6). Cluster 2 (Nimba, Margibi, Grand Gedeh, some of Gbarpolu, and a few districts in Grand Cape Mount, Montserrado, Sinoe, and Maryland Counties) was the least vulnerable cluster with negative values for water quality/proximity to medical facilities (Factor 1) and food quantity (Factor 3).

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