Too far to walk – calibrating distances to maternity health facilities for women in Ghana using GIS tools Zoë Matthews

Coauthors Fiifi Amoako-Johnson, Social Statistics, University of Southampton Faustina Frempong-Aiguah, (RIPS) University of Ghana Peter Gething, Spatial Epidemiology, Dept Zoology, University of Oxford Peter Atkinson, School of Geography, University of Southampton Angela Baschieri, London School of Hygiene and Tropical Medicine Philomena E. Nyarko, (RIPS) University of Ghana and GSS Francis Nii-Amoo Dodoo, (RIPS) University of Ghana Jane C. Falkingham, Centre for Population Change, University of Southampton Patrick Aboagye, Ghana Health Services, Accra, Ghana

Acknowledgements ESRC/DFID – for funding project IMMPACT – for use of survey data from Ghana CERSGIS – University of Ghana, for use of GIS data Ghana Statistical Service – for use of GIS and facility data

Outline Background Ghana Aims Methods Stage 1: Build a model of physical access Stage 2: Calibrate the model using survey data of actual physical access in a small area Stage 3: Apply nationally Building and calibrating a model to measure physical access to facilities Data sources in Ghana Results – not yet! Why the results (when we get them!) will be an advance – and how they will be useful

Policy efforts to break down barriers to effective coverage COST 2003 Fee Exemption Policy 4 regions targeted for the policy Policy ended in 2005 but free care for all pregnant women via national insurance scheme announced 2007/2008 DISTANCE CHPS strategy for reaching the unreached is part of the national poverty reduction strategy. CHPS districts deploy professional health workers to provide community-based health care, including safe motherhood and family planning 27.6% of districts have brought health services to communities QUALITY MAF plans to upgrade family planning, skilled delivery and EmONC Includes equipment, improved education and support for health workers as well as commodities and support for governance

Existing data: health system infrastructure Average distance to nearest hospital As measured on census 2000…but these are not maternity facilities

Existing data

Journey time (minutes) Aim To give an accurate picture of distances to maternal health facilities in Ghana – down to small areas – nationwide. To provide ‘cost-surface’ maps of Ghana for maternity facilities as has been done in Kenyan districts for malaria WHY? To facilitate understanding of the extent to which distance, or distance related factors are a barrier to use To monitor the success of policies For planning and targetting Journey time (minutes)

How? Stage 1: Build a model of distance to facility using GIS catchment techniques Stage 2: Calibrate model using survey data on a small area with actual travel distances and times Stage 3: Apply nationally

Data sources Stage 1 Stage 2 Stage 3 Facility locations Land cover Transport routes Elevation map Stage 2 Actual distances for a smaller area – survey data Should include residence location, facility location and actual time/distance Should be specific to maternity Stage 3 As for Stage 1 – covering whole country Accurately parameterised model from Stage 2 incorporating facility choice and bypassing

Stage 1: for Ghana we have Facility map Land cover including: Road and path networks Natural barriers – lakes, rivers, swamps, nat.parks by season Contour maps for gradient

Building a GIS model to measure physical access to health facilities How far? Which is nearest? Facility A Facility B 12

Simple approach is Euclidean distance Catchment modelling Simple approach is Euclidean distance Facility A Facility B 13

BUT Euclidean distance not a good measure Affected by: Catchment modelling BUT Euclidean distance not a good measure Affected by: Facility A Facility B 14

Catchment modelling …natural barriers Facility A Facility B 15

…elevation / gradient / slope Catchment modelling …elevation / gradient / slope Facility B Facility A Facility B 16

…transport network Catchment modelling Facility B Facility A 17

SO journey time likely to be a better metric Catchment modelling SO journey time likely to be a better metric Facility B Facility A Facility B 18

+ = Modelling step 1: production of ‘impedance’ grid Natural barriers Catchment modelling Modelling step 1: production of ‘impedance’ grid + = Natural barriers Roads/paths Impedance 19

Spatial analysis and GIS for the mapping of malaria in Kenya Catchment modelling Step 2: Incorporation of effect of gradient on speeds Used variation of Naysmith’s rule does your model use Naismiths rule to estimate the time it takes to walk a route? Yes, the original version we coded up for Kenya used a slight elaboration on Naysmith's rule to calibrate walking time and effect of gradient Spatial analysis and GIS for the mapping of malaria in Kenya 20

Journey time (minutes) Catchment modelling Step 3: Design of algorithm to calculate journey-time Assumes parameters based on number of gradient categories, base speeds by mode of transport and conversion of travel speeds to friction/impedance Uses 100m by 100m raster grid Uses region-growing approach from each facility Calculates cumulative sum of journey time to each pixel Incorporates a facility choice component Journey time (minutes) does the model assign a friction value to each pixel of the various raster image files according to surface type to generate the cost to move across each pixel? Yep, exactly that. 'Friction' is defined more specifically as 'time in seconds to traverse the pixel' and it varied depending on which direction you were going in (because a sloped pixel is uphill in one direction and downhill in the other) and also whether you went diagonally across it (which is a further distance than if going straight across). Does it use the cost module in Idrisi (Clark labs) applied to obtain travel cost image then scaled to minutes travel time???? Nope, our algorithm was written from scratch in C - no such algorithms were available in GIS at the time. Also the elaborations described above are not always coded in to t he GIS versions.. 21

Stage 2: Calibrate using real data from a survey IMMPACT out-of-pocket-costs survey in two regions Volta and Central – over 2,000 women surveyed Includes distances to maternity facilities and times as well as forms of transport To calibrate: Repeatedly COMPARE different versions (with alternative parameterisations) of the MODEL applied to locations in the survey with the REAL TIMES from the survey… then choose the optimum best-performing model with least squared differences Proceed to Stage 3…APPLY MODEL MORE WIDELY Volta Central Total Vaginal Delivery HF 600 300 900 Vaginal delivery H/TBA 600 300 900 C Section 300 150 450 Total 1500 750 2250 A two-stage approach was used to identify women for the household cost survey. The first stage selected health facilities operating immunisation programmes and child welfare clinics in all the six districts identified for the evaluation in Central and Volta regions and sampled women falling into the sampling frame. In the second stage the sampled women were followed to their homes to administer the household cost questionnaire.

Results: travel time

Results: % of women who live within 2 or 4 hrs travel time