Table 1.
Studies comparing different methods of calculating travel impedance to health services
| # Ref | Author (year) Country | Data sources | Impedance measures | Outcome | Comparison method | Favoured measure/conclusion |
|---|---|---|---|---|---|---|
|
Low- and middle-income countries | ||||||
| 1 [14] |
Okwaraji (2012) Ethiopia |
1. Geocoded households |
1. Euclidean distance |
Under 5 child mortality |
1. Correlation coefficient |
Actual travel distance |
| 2. Geocoded health center |
2. Raster travel time |
2. Compare measures of effect |
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| 3. Land cover, Ethiopia Mapping Agency |
3. Actual travel distance |
|
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| 4. Digital elevation model from Shuttle Radar Topography Mission (NASA) | ||||||
| 2 [15] |
Noor (2006) Kenya |
1. Geocoded homesteads |
1. Euclidean distance |
Predicted specific facility use by febrile children; Proportion of people within one hour of HF |
1. Kappa statistic (agreement between predicted and observed facility use) |
Raster travel time (transport network model) adjusted for competition |
| 2. Geocoded HFs |
2. Raster travel time (termed transport network model) |
2. Linear regression (R2) |
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| 3. Population density at 100 m resolution (Kenya Census 1999) |
3. Raster travel time (transport network model), adjusted for competition between facilities |
3. Scatter plots |
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| 4. Road network (Africover, plus manual updates) |
|
4. Spatial mapping |
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| 5. Topography (Africover, plus updates & Livestock research institute, Nairobi & Park & reserve digital map from Kenya Wildlife Service) | ||||||
| 3 [16] |
Costa (2003) Brazil |
1. Admissions data from national public health database |
1. Euclidean distance |
None |
1. Maximum difference in distances |
“Real” distance |
| 2. Extracted district of residence from postal codes from national database |
2. “Real” distance, estimated as city bus itinerary from district centroid to hospital, adjusted for residence district area |
|||||
| 3. GIS coordinates for 14 public hospitals | ||||||
| 4. City transit network map, bus routes | ||||||
|
High-income countries | ||||||
| 3 [17] |
*Cudnik (2012) USA |
1. Patient location via EMS data |
1. Euclidean distance |
None |
1. Wilcoxon signed rank test |
Reasonable to use Euclidean distance |
| 2. HF location via addresses |
2. Network distance |
2. Spearman rank |
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| 3. Road network (ArcGIS StreetMap; commercially available) |
3. Actual transport distance (in EMS vehicle) |
3. Linear regression (R2) |
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| 4 [9] |
*Delamater (2012) USA |
1. Population (US Census 2010) |
1. Network travel time |
Proportion of state classified as limited access area (LAA) |
1. Percentage change in proportion LAA |
Depends on research question |
| 2. Road network (Michigan Center for Geographic Information 2009) |
2. Network distance |
2. Mapping |
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| 3. Raster travel time | ||||||
| 4. Raster distance | ||||||
| 5 [18] |
~*Lian (2012) USA |
1. Incident breast cancer cases (Missouri cancer registry) |
1. Network travel time |
Incident odds of late-stage breast cancer |
1. Spearman rank |
2SFCA |
| 2. Population coordinates (US Census 2000) |
2. Average of 5 shortest network travel times |
2. Kappa coefficient |
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| 3. HF coordinates (FDA) |
3. Service density |
3. Moran I index |
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| 4. Road Network (US Census/ TIGER) |
4. Two-step floating catchment area (2SFCA) |
4. Comparison of effect measures on risk of outcome |
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| 6 [19] |
*Jones (2010) USA |
1. Population location (Insurance claims data) |
1. Euclidean distance |
None |
1. Wilcoxon’s signed rank sum tests |
Network distance |
| 2. HF location via addresses |
2. Network distance |
2. Scatter plots |
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| 3. Road network (no source listed) | ||||||
| 7 [20] |
*Apparicio (2008) Canada |
1. Population coordinates (Statistics Canada) |
1. Euclidean distance |
None |
1. Spearman rank |
Network distance |
| 2. HF coordinates (Quebec Ministry of Health and Social services) |
2. Manhattan distance |
|
2. Absolute differences in measures |
|||
| 3. Road network (CanMap street files, commercially available) |
3. Network distance |
|
3. Spatial mapping |
|||
| 4. Network travel time | ||||||
| 8 [21] |
Fone (2006) UK |
1. Population via postal survey from Gwent Health Authority |
1. Euclidean distance |
Perceived accessibility |
1. Kruskal-Wallis |
Minimal advantage in using sophisticated measures |
| 2. Population location via census |
2. Network travel time |
|
2. Spearman rank |
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| 3. HF locations from Gwent Health Authority |
3. Network distance |
|||||
| 4. Road network (MapInfo Drivetime software, commercially available) |
|
|||||
| 9 [22] |
Haynes (2006) UK |
1. Hospital-based patient questionnaire (with post-codes) |
1. Euclidean distance |
None |
1. Spearman rank |
No evidence that GIS estimates better than Euclidean |
| 2. Geocoded HF location |
2. Network travel time |
2. Linear regression (R2) |
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| 3. Road network (Ordinance Survey Meridian, digital map) |
3. Actual travel time |
|||||
| 10 [23] | *Fortney (2000) USA | 1. Population location from previous study sample |
1. Euclidean Distance |
None (travel time as gold standard) | 1. Correlation coefficients |
Marginal gains in accuracy using network measures |
| 2. HF location from physician desk reference database (State licensing board) |
2. Network distance |
2. Linear regression |
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| 3. Road network (US Census Bureau) | 3. Differences between measures | |||||
Included studies compared Euclidean distance to at least one other method of calculating travel impedance included in our comparison, or compared two other methods used in our comparison (~denotes an exception). Abbreviations: HF = health facility; FDA = US Food and Drug Administration; EMS = emergency medical service; 2SFCA = two-step floating catchment area; LAA = limited access area; NASA = US National Space Agency. *Studies also compared population aggregation methods (e.g. address, census area, census block post/zip-code centroid etc., details not included).