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Table 1 Studies comparing different methods of calculating travel impedance to health services

From: Methods to measure potential spatial access to delivery care in low- and middle-income countries: a case study in rural Ghana

# 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
3. Land cover, Ethiopia Mapping Agency 3. Actual travel distance  
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)
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
4. Road network (Africover, plus manual updates)   4. Spatial mapping
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
3. Road network (ArcGIS StreetMap; commercially available) 3. Actual transport distance (in EMS vehicle) 3. Linear regression (R2)
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
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
3. HF coordinates (FDA) 3. Service density 3. Moran I index
4. Road Network (US Census/ TIGER) 4. Two-step floating catchment area (2SFCA) 4. Comparison of effect measures on risk of outcome
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
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
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)
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
3. Road network (US Census Bureau) 3. Differences between measures
  1. 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).