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AccessMod 3.0: computing geographic coverage and accessibility to health care services using anisotropic movement of patients.

Ray N, Ebener S - Int J Health Geogr (2008)

Bottom Line: Four major types of analysis are available in AccessMod: (1) modeling the coverage of catchment areas linked to an existing health facility network based on travel time, to provide a measure of physical accessibility to health care; (2) modeling geographic coverage according to the availability of services; (3) projecting the coverage of a scaling-up of an existing network; (4) providing information for cost effectiveness analysis when little information about the existing network is available.By incorporating the demand (population) and the supply (capacities of heath care centers), AccessMod provides a unifying tool to efficiently assess the geographic coverage of a network of health care facilities.This tool should be of particular interest to developing countries that have a relatively good geographic information on population distribution, terrain, and health facility locations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Information, Evidence and Research, World Health Organization, 20 av, Appia, 1211 Geneva 27, Switzerland. nicolas.ray@zoo.unibe.ch

ABSTRACT

Background: Access to health care can be described along four dimensions: geographic accessibility, availability, financial accessibility and acceptability. Geographic accessibility measures how physically accessible resources are for the population, while availability reflects what resources are available and in what amount. Combining these two types of measure into a single index provides a measure of geographic (or spatial) coverage, which is an important measure for assessing the degree of accessibility of a health care network.

Results: This paper describes the latest version of AccessMod, an extension to the Geographical Information System ArcView 3.x, and provides an example of application of this tool. AccessMod 3 allows one to compute geographic coverage to health care using terrain information and population distribution. Four major types of analysis are available in AccessMod: (1) modeling the coverage of catchment areas linked to an existing health facility network based on travel time, to provide a measure of physical accessibility to health care; (2) modeling geographic coverage according to the availability of services; (3) projecting the coverage of a scaling-up of an existing network; (4) providing information for cost effectiveness analysis when little information about the existing network is available. In addition to integrating travelling time, population distribution and the population coverage capacity specific to each health facility in the network, AccessMod can incorporate the influence of landscape components (e.g. topography, river and road networks, vegetation) that impact travelling time to and from facilities. Topographical constraints can be taken into account through an anisotropic analysis that considers the direction of movement. We provide an example of the application of AccessMod in the southern part of Malawi that shows the influences of the landscape constraints and of the modes of transportation on geographic coverage.

Conclusion: By incorporating the demand (population) and the supply (capacities of heath care centers), AccessMod provides a unifying tool to efficiently assess the geographic coverage of a network of health care facilities. This tool should be of particular interest to developing countries that have a relatively good geographic information on population distribution, terrain, and health facility locations.

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Related in: MedlinePlus

Speed correction for walking and bicycling depending on slope intensity.
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Figure 3: Speed correction for walking and bicycling depending on slope intensity.

Mentions: In a second step, a travelling scenario must be set by the user. This scenario defines which of the landcover are used for patient travel, and what are the travelling speed and mean of transportation on each landcover. Each travelling speed (in km/h) can be set to any value by the user, but because travelling speeds are strong determinant of the realized extent of catchment areas, they should be chosen carefully based on known or supposed travelling habits of the population under consideration. Typically, relatively high speeds should be assigned to the road network, with different roads (e.g. secondary and primary roads, highways) having different travelling speeds. For other landcover types (e.g. forest, open bush), a mean walking speed on flat surface (e.g. 5 km/h) or mean bicycling speed on flat ground (e.g. 10 km/h) can be set. Two anisotropic speed correction models (using slopes derived for the DEM) can further be set by the user. A first model corrects for the walking speeds in hilly terrain and is derived from the Tobler's formula [17]. This correction basically decreases the effective speed of walking for up-slope and down-slope walking as the slope increases, while slightly increasing the effective speed for a slightly negative slope when walking down-slope. The second available model deals with speed correction for bicycling. We derived this correction using information on bicycle speed power calculation [18,23] and it assumes that the increased speed due to negative slope does not exceed twice the speed on flat ground. A graphical representation of the correction factors for walking and bicycling is shown in Figure 3.


AccessMod 3.0: computing geographic coverage and accessibility to health care services using anisotropic movement of patients.

Ray N, Ebener S - Int J Health Geogr (2008)

Speed correction for walking and bicycling depending on slope intensity.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2651127&req=5

Figure 3: Speed correction for walking and bicycling depending on slope intensity.
Mentions: In a second step, a travelling scenario must be set by the user. This scenario defines which of the landcover are used for patient travel, and what are the travelling speed and mean of transportation on each landcover. Each travelling speed (in km/h) can be set to any value by the user, but because travelling speeds are strong determinant of the realized extent of catchment areas, they should be chosen carefully based on known or supposed travelling habits of the population under consideration. Typically, relatively high speeds should be assigned to the road network, with different roads (e.g. secondary and primary roads, highways) having different travelling speeds. For other landcover types (e.g. forest, open bush), a mean walking speed on flat surface (e.g. 5 km/h) or mean bicycling speed on flat ground (e.g. 10 km/h) can be set. Two anisotropic speed correction models (using slopes derived for the DEM) can further be set by the user. A first model corrects for the walking speeds in hilly terrain and is derived from the Tobler's formula [17]. This correction basically decreases the effective speed of walking for up-slope and down-slope walking as the slope increases, while slightly increasing the effective speed for a slightly negative slope when walking down-slope. The second available model deals with speed correction for bicycling. We derived this correction using information on bicycle speed power calculation [18,23] and it assumes that the increased speed due to negative slope does not exceed twice the speed on flat ground. A graphical representation of the correction factors for walking and bicycling is shown in Figure 3.

Bottom Line: Four major types of analysis are available in AccessMod: (1) modeling the coverage of catchment areas linked to an existing health facility network based on travel time, to provide a measure of physical accessibility to health care; (2) modeling geographic coverage according to the availability of services; (3) projecting the coverage of a scaling-up of an existing network; (4) providing information for cost effectiveness analysis when little information about the existing network is available.By incorporating the demand (population) and the supply (capacities of heath care centers), AccessMod provides a unifying tool to efficiently assess the geographic coverage of a network of health care facilities.This tool should be of particular interest to developing countries that have a relatively good geographic information on population distribution, terrain, and health facility locations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Information, Evidence and Research, World Health Organization, 20 av, Appia, 1211 Geneva 27, Switzerland. nicolas.ray@zoo.unibe.ch

ABSTRACT

Background: Access to health care can be described along four dimensions: geographic accessibility, availability, financial accessibility and acceptability. Geographic accessibility measures how physically accessible resources are for the population, while availability reflects what resources are available and in what amount. Combining these two types of measure into a single index provides a measure of geographic (or spatial) coverage, which is an important measure for assessing the degree of accessibility of a health care network.

Results: This paper describes the latest version of AccessMod, an extension to the Geographical Information System ArcView 3.x, and provides an example of application of this tool. AccessMod 3 allows one to compute geographic coverage to health care using terrain information and population distribution. Four major types of analysis are available in AccessMod: (1) modeling the coverage of catchment areas linked to an existing health facility network based on travel time, to provide a measure of physical accessibility to health care; (2) modeling geographic coverage according to the availability of services; (3) projecting the coverage of a scaling-up of an existing network; (4) providing information for cost effectiveness analysis when little information about the existing network is available. In addition to integrating travelling time, population distribution and the population coverage capacity specific to each health facility in the network, AccessMod can incorporate the influence of landscape components (e.g. topography, river and road networks, vegetation) that impact travelling time to and from facilities. Topographical constraints can be taken into account through an anisotropic analysis that considers the direction of movement. We provide an example of the application of AccessMod in the southern part of Malawi that shows the influences of the landscape constraints and of the modes of transportation on geographic coverage.

Conclusion: By incorporating the demand (population) and the supply (capacities of heath care centers), AccessMod provides a unifying tool to efficiently assess the geographic coverage of a network of health care facilities. This tool should be of particular interest to developing countries that have a relatively good geographic information on population distribution, terrain, and health facility locations.

Show MeSH
Related in: MedlinePlus