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Using remotely sensed night-time light as a proxy for poverty in Africa.

Noor AM, Alegana VA, Gething PW, Tatem AJ, Snow RW - Popul Health Metr (2008)

Bottom Line: To assess the effectiveness of health interventions it is critical to monitor the spatial and temporal changes in the health indicators of populations and outcomes across varying levels of poverty.At the Administrative 1 level all the NTL metrics distinguished between the most poor and least poor quintiles with greater precision compared to intermediate quintiles.Metrics of the brightness of NTL data offer a robust and inexpensive alternative to asset-based poverty indices derived from survey data at the Administrative 1 level in Africa.

View Article: PubMed Central - HTML - PubMed

Affiliation: Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI - University of Oxford - Wellcome Trust Collaborative Programme, Kenyatta National Hospital Grounds (behind NASCOP), P,O, Box 43640-00100, Nairobi, Kenya. anoor@nairobi.kemri-wellcome.org.

ABSTRACT

Background: Population health is linked closely to poverty. To assess the effectiveness of health interventions it is critical to monitor the spatial and temporal changes in the health indicators of populations and outcomes across varying levels of poverty. Existing measures of poverty based on income, consumption or assets are difficult to compare across geographic settings and are expensive to construct. Remotely sensed data on artificial night time lights (NTL) have been shown to correlate with gross domestic product in developed countries.

Methods: Using national household survey data, principal component analysis was used to compute asset-based poverty indices from aggregated household asset variables at the Administrative 1 level (n = 338) in 37 countries in Africa. Using geographical information systems, mean brightness of and distance to NTL pixels and proportion of area covered by NTL were computed for each Administrative1 polygon. Correlations and agreement of asset-based indices and the three NTL metrics were then examined in both continuous and ordinal forms.

Results: At the Administrative 1 level all the NTL metrics distinguished between the most poor and least poor quintiles with greater precision compared to intermediate quintiles. The mean brightness of NTL, however, had the highest correlation coefficient with the asset-based wealth index in continuous (Pearson correlation = 0.64, p < 0.01) and ordinal (Spearman correlation = 0.79, p < 0.01; Kappa = 0.64) forms.

Conclusion: Metrics of the brightness of NTL data offer a robust and inexpensive alternative to asset-based poverty indices derived from survey data at the Administrative 1 level in Africa. These could be used to explore economic inequity in health outcomes and access to health interventions at sub-national levels where household assets data are not available at the required resolution.

No MeSH data available.


Related in: MedlinePlus

Administrative 1 unit boundary maps of Africa showing: a) the distribution of night time lights for the year 2000; b) availability of assets data for 338 units in 37 countries. The maps also show areas of zero population density derived from GRUMP surface.
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Figure 1: Administrative 1 unit boundary maps of Africa showing: a) the distribution of night time lights for the year 2000; b) availability of assets data for 338 units in 37 countries. The maps also show areas of zero population density derived from GRUMP surface.

Mentions: The Global Rural Urban Mapping Project (GRUMP) is the most recent and highest resolution source of human population distribution data at the continental level [25]. This database is created from a substantially larger number of administrative data units, and has been shown to provide a higher level of accuracy, than other population data products [26,27]. GRUMP provides global gridded population density estimates at ~1 × 1 km spatial resolution as described in detail elsewhere [25,28]. Those areas of Africa defined by GRUMP as having zero population were vectorized to form polygons (Figure 1) which were then used to re-define the habitable area within each Administrative 1 unit for subsequent extraction and analysis.


Using remotely sensed night-time light as a proxy for poverty in Africa.

Noor AM, Alegana VA, Gething PW, Tatem AJ, Snow RW - Popul Health Metr (2008)

Administrative 1 unit boundary maps of Africa showing: a) the distribution of night time lights for the year 2000; b) availability of assets data for 338 units in 37 countries. The maps also show areas of zero population density derived from GRUMP surface.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2577623&req=5

Figure 1: Administrative 1 unit boundary maps of Africa showing: a) the distribution of night time lights for the year 2000; b) availability of assets data for 338 units in 37 countries. The maps also show areas of zero population density derived from GRUMP surface.
Mentions: The Global Rural Urban Mapping Project (GRUMP) is the most recent and highest resolution source of human population distribution data at the continental level [25]. This database is created from a substantially larger number of administrative data units, and has been shown to provide a higher level of accuracy, than other population data products [26,27]. GRUMP provides global gridded population density estimates at ~1 × 1 km spatial resolution as described in detail elsewhere [25,28]. Those areas of Africa defined by GRUMP as having zero population were vectorized to form polygons (Figure 1) which were then used to re-define the habitable area within each Administrative 1 unit for subsequent extraction and analysis.

Bottom Line: To assess the effectiveness of health interventions it is critical to monitor the spatial and temporal changes in the health indicators of populations and outcomes across varying levels of poverty.At the Administrative 1 level all the NTL metrics distinguished between the most poor and least poor quintiles with greater precision compared to intermediate quintiles.Metrics of the brightness of NTL data offer a robust and inexpensive alternative to asset-based poverty indices derived from survey data at the Administrative 1 level in Africa.

View Article: PubMed Central - HTML - PubMed

Affiliation: Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI - University of Oxford - Wellcome Trust Collaborative Programme, Kenyatta National Hospital Grounds (behind NASCOP), P,O, Box 43640-00100, Nairobi, Kenya. anoor@nairobi.kemri-wellcome.org.

ABSTRACT

Background: Population health is linked closely to poverty. To assess the effectiveness of health interventions it is critical to monitor the spatial and temporal changes in the health indicators of populations and outcomes across varying levels of poverty. Existing measures of poverty based on income, consumption or assets are difficult to compare across geographic settings and are expensive to construct. Remotely sensed data on artificial night time lights (NTL) have been shown to correlate with gross domestic product in developed countries.

Methods: Using national household survey data, principal component analysis was used to compute asset-based poverty indices from aggregated household asset variables at the Administrative 1 level (n = 338) in 37 countries in Africa. Using geographical information systems, mean brightness of and distance to NTL pixels and proportion of area covered by NTL were computed for each Administrative1 polygon. Correlations and agreement of asset-based indices and the three NTL metrics were then examined in both continuous and ordinal forms.

Results: At the Administrative 1 level all the NTL metrics distinguished between the most poor and least poor quintiles with greater precision compared to intermediate quintiles. The mean brightness of NTL, however, had the highest correlation coefficient with the asset-based wealth index in continuous (Pearson correlation = 0.64, p < 0.01) and ordinal (Spearman correlation = 0.79, p < 0.01; Kappa = 0.64) forms.

Conclusion: Metrics of the brightness of NTL data offer a robust and inexpensive alternative to asset-based poverty indices derived from survey data at the Administrative 1 level in Africa. These could be used to explore economic inequity in health outcomes and access to health interventions at sub-national levels where household assets data are not available at the required resolution.

No MeSH data available.


Related in: MedlinePlus