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Modeling population access to New Zealand public hospitals.

Brabyn L, Skelly C - Int J Health Geogr (2002)

Bottom Line: Average time and distance statistics have been calculated for local populations by modeling the total travel of a population if everybody visited a hospital once.These types of statistics can be generated for different population groups and enable comparisons to be made between regions.This study has shown that the northern and southern parts of New Zealand have high average travel times to hospital services.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Geography, University of Waikato, New Zealand. larsb@waikato.ac.nz

ABSTRACT
This paper demonstrates a method for estimating the geographical accessibility of public hospitals. Cost path analysis was used to determine the minimum travel time and distance to the closest hospital via a road network. This analysis was applied to 38,000 census enumeration district centroids in New Zealand allowing geographical access to be linked to local populations. Average time and distance statistics have been calculated for local populations by modeling the total travel of a population if everybody visited a hospital once. These types of statistics can be generated for different population groups and enable comparisons to be made between regions. This study has shown that the northern and southern parts of New Zealand have high average travel times to hospital services.

No MeSH data available.


Travel Distance in Kilometers to the Closest Hospital by Census Centroids. The District Health Board Boundaries are also shown.
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Figure 3: Travel Distance in Kilometers to the Closest Hospital by Census Centroids. The District Health Board Boundaries are also shown.

Mentions: The travel distance and time to the closest hospital for each census centroid are represented in Figures 3 and 4. These figures show the raw data that results from the analysis (the DHB boundaries are also shown). These results can be aggregated to many different regional management units, such District Health Boards (DHB). Each census centroid represents the usual residential (night time) population, which can vary from 0 to 768 people. The distance and time to the closest hospital can be multiplied by the population of the centroid to give the total distance and time traveled if everyone in the centroid visited the closest hospital once. These totals can then be summed for each DHB, and then divided by the usual residential population of each DHB to give the average distance and time traveled by each DHB if everyone visited the closest hospital once. Figure 5 shows the average travel time by DHB if everyone visited the closest hospital once. The shortest path by distance may be totally different to the shortest path by time because of the different travel speeds of the roads [16]. The use of time and the interrelationship between space and time has an important affect on choice [17] so travel time is more important than travel distance.


Modeling population access to New Zealand public hospitals.

Brabyn L, Skelly C - Int J Health Geogr (2002)

Travel Distance in Kilometers to the Closest Hospital by Census Centroids. The District Health Board Boundaries are also shown.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC149398&req=5

Figure 3: Travel Distance in Kilometers to the Closest Hospital by Census Centroids. The District Health Board Boundaries are also shown.
Mentions: The travel distance and time to the closest hospital for each census centroid are represented in Figures 3 and 4. These figures show the raw data that results from the analysis (the DHB boundaries are also shown). These results can be aggregated to many different regional management units, such District Health Boards (DHB). Each census centroid represents the usual residential (night time) population, which can vary from 0 to 768 people. The distance and time to the closest hospital can be multiplied by the population of the centroid to give the total distance and time traveled if everyone in the centroid visited the closest hospital once. These totals can then be summed for each DHB, and then divided by the usual residential population of each DHB to give the average distance and time traveled by each DHB if everyone visited the closest hospital once. Figure 5 shows the average travel time by DHB if everyone visited the closest hospital once. The shortest path by distance may be totally different to the shortest path by time because of the different travel speeds of the roads [16]. The use of time and the interrelationship between space and time has an important affect on choice [17] so travel time is more important than travel distance.

Bottom Line: Average time and distance statistics have been calculated for local populations by modeling the total travel of a population if everybody visited a hospital once.These types of statistics can be generated for different population groups and enable comparisons to be made between regions.This study has shown that the northern and southern parts of New Zealand have high average travel times to hospital services.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Geography, University of Waikato, New Zealand. larsb@waikato.ac.nz

ABSTRACT
This paper demonstrates a method for estimating the geographical accessibility of public hospitals. Cost path analysis was used to determine the minimum travel time and distance to the closest hospital via a road network. This analysis was applied to 38,000 census enumeration district centroids in New Zealand allowing geographical access to be linked to local populations. Average time and distance statistics have been calculated for local populations by modeling the total travel of a population if everybody visited a hospital once. These types of statistics can be generated for different population groups and enable comparisons to be made between regions. This study has shown that the northern and southern parts of New Zealand have high average travel times to hospital services.

No MeSH data available.