Towards universal coverage in DRC: spatial and financial barriers to accessing care

1. Towards universal coverage in DRC: spatial and financial barriers to accessing care Presented by Caryn Bredenkamp Health Economist, World Bank, Washington DC based on analysis by Brian Blankespoor, Caryn Bredenkamp, Patrick Mullen, DanicOstiguy and WalyWane African Health Economics and Policy Association (AfHEA) Dakar, Senegal 16 March 2011

2. Health outcomes are improving, but remain poor: • - 45.5% of children are chronically malnourished • - under-five mortality is 148 per 1,000 live births • - maternal mortality ratio is 549 per 100,000 live births Coverage of many high impact interventions, especially preventive, is low: • 31% of one-year-old children arefully immunized • 6% of under-five children sleep under insecticide-treated bednets => Demand-side barriers - financial and spatial - to accessing care Not on track to meet the MDGs 1c, 4 and 5 Source: DHS 2007

3. Explore how spatial data and GIS techniques can be used to broaden our understanding of physical/spatial access to health care • Identify the areas that are insufficiently served by primary health care services in order to inform decisions about service expansion • Highlight financial costs of seeking care Objective

4. Study area Site of a PBF intervention District of Haut-Katanga ~1.350.000 population 5 hospitals 3 hospital-likereferralcenters 4 referralhealthcenters 148 healthcenters and healthposts 85 public, 31 faith-based facilities 44 private facilities

5. Baseline data from PBF impact evaluation – Sept-Oct 2009 - Household survey - Community survey - Patient exit interviews - Facility surveys – with staff interviews - GPS coordinates • Secondary GPS data Data sources

6. Measuring spatial access to care

7. Primary data collection: • GPS coordinates of health facilities were collected - as part of the baseline • Hand-drawn maps of the boundaries of health zones and health areas were obtained from the chief doctors of the health zones in Haut-Katanga Data description I

8. Secondary data collection: Geographic Information System (GIS) data • Road networks from multiple sources • Village locations • Administrative units • Hydrography • Elevation • Slope Data description II

9. Methods I • Obtain GPS coordinates of health facilities from baseline survey • Create health area boundaries by digitizing paper maps that includes population information

10. 3. Define catchment areaIncludes all villages/settlements that are closest to the particular health facility relative to all other facilities • 4. Integrate road network information to estimate travel time. First, by using data on the GPS coordinates of health facilities and villages/settlements, and adding known transportation routes, we are able to build a spatial model that estimates average travel time to the health centers for each village using GIS data based on an approach by Nelson (2008). • 5. Summarize information on population to display the distribution of population among the health centers Methods II Nelson, A. 2008. Travel time to major cities: A global map of Accessibility. Global Environment Monitoring Unit - Joint Research Centre of the European Commission, Ispra Italy. Available at http://gem.jrc.ec.europa.eu

11. Results

12. Inequalities in distribution of facilities • Non-standard catchment areas: vary tremendously, both in terms of size (28 – 5,900 km2) and population size (5 000 people to 60 000 people). • Most facilities are located along the main roads - especially public health facilities. • The distribution of facility types is very uneven Most private facilities are concentrated around Lubumbashi. Mix of facility types also varies a lot

13. Inequalities in travel time Translate road-type information into travel time using method of Nelson (2008) => map Assume even population distribution by health area, find: Population with more than 4 hours travel time to nearest facility: 610,000 Population with less than 4 hours travel time: 483,000 Settlement point population would improve the health area population estimates

14. Adding information from household surveys and exit interviews A more nuanced picture of access

15. Why did you visit this facility? Patient exit interviews confirm that distance is a key determinant of service utilization Source: Patient exit interviews

16. Most “users” / patients don’t travel far to get there

17. Most travel by foot Implication: It is likely that most households in the “green areas” are hardly accessing care (as expected)

18. This is confirmed by travel cost information

19. Costs of care dominated by consultations and medicines Source: Household survey

20. Number of people using primary care in the last four weeks: • Public 436 • Private 247 • Faith-based 80 • Traditional 113 • Total 876 out of 6817 people (12.9%) So, given the barriers, how high are utilization rates? Source: Household survey

21. Conclusion • The population in Haut-Katanga faces substantial spatial barriers to accessing care. These include great distances to primary and referral health facilities and a poor road network that contribute to long travel times • With relatively little additional data collection – and even without household surveys - much can be learnt about a population’s spatial access to care • But, patient interviews and household surveys add useful additional information – costs of accessing care, and utilization rates

22. Funding for the data collection of this project was provided by the Health Results Innovation Trust Fund (HRITF), World Bank. The findings, interpretations, and conclusions expressed on this poster are entirely those of the authors. • For further information • Please contact bblankespoor@worldbank.orgor cbredenkamp@worldbank.org. • More information on related research projects can be obtained at http://econ.worldbank.org; http://www.worldbank.org/hnp; andhttp://www.rbfhealth.org Acknowledgments