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

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

Policy efforts to break down

barriers to effective coverage

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

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

Existing data: health system infrastructureAverage distance to nearest hospital

As measured on census 2000…but these are not maternity facilities

Existing data

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

– 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

• Simple approach is Euclidean distance

Catchment modelling

Facility A Facility B

• BUT Euclidean distance not a good measure• Affected by:

Catchment modelling

Facility A Facility B

Catchment modelling

Facility A Facility B

…natural barriers

Catchment modelling

Facility A Facility B

Facility B

…elevation / gradient / slope

Catchment modelling

Facility A Facility B

Facility B

…transport network

• SO journey time likely to be a better metric

Catchment modelling

Facility A Facility B

Facility B

+ =

Catchment modelling

• Modelling step 1: production of ‘impedance’ grid

Natural barriers Roads/paths Impedance

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

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)

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

Results:travel time

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