Empowering Local Decision Makers in Iringa,Tanzania: PLACE Lite and the Iringa Participatory Mapping Exercise
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Marc Cunningham shares how the MEASURE Evaluation project used participatory mapping activities as a tool to promote local use of data to increase access to HIV services in Iringa, Tanzania.
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Empowering Local Decision Makers in Iringa,Tanzania: PLACE Lite and the Iringa Participatory Mapping Exercise
- 1. Marc Cunningham, MPH MEASURE Evaluation John Snow, Inc. (JSI) 10 June 2015 Measurement for Accountability for Results in Health Summit Washington, DC USA Empowering Local Decision Makers in Iringa, Tanzania: PLACE Lite and the Iringa Participatory Mapping Exercise
- 2. Better data are local data… …that are used for local decisions
- 3. 3 Iringa 9% Njombe 15% National prevalence: 5% HIV Prevalence: Iringa and Njombe Regions 2011-2012 Tanzania HIV/AIDS and Malaria Indicator Survey
- 4. 4 Iringa and Njombe Regions
- 5. Priorities for Local AIDS Control Efforts (PLACE) Lite Iringa, Tanzania Iringa Participatory Mapping Exercise: Service Catchment Areas
- 6. “Truck stops are usually visited by sex workers in the evenings, when more than 70 trucks are parked before heading to their eventual destinations.” Qualitative Quantitative (Iringa Urban) PLACE Lite
- 9. Patron characteristics (%) Attended by migrant workers 89.5 Attended by truck drivers 81.5 Attended by road workers 70.8 Attended by plantation workers 63.1 Attended by miners 0.0 Prevention activities (%) Condoms available at venue 21.5 Condoms available within 10 min walk 47.7
- 11. “Several times [when] we allocate [implementing partners] to some area, especially the distant areas they say they cannot go there because they do not have enough fund to operate to that area. .... This results to some areas being well served while others underserved.” Iringa Participatory Mapping Exercise
- 12. Iringa Participatory Mapping Exercise
- 13. Facility Based Services •Prevention of mother to child transmission •Voluntary Counselling and Testing •Care and Treatment •Voluntary Male Medical Circumcision Community Based Services •Prevention and outreach •Home based care •Orphans and vulnerable children
- 15. Audiences •USAID|Tanzania •MOHSW •CBOs and NGOs Information Products •Reports •Online maps •Regional and District Workshops Sharing the Data
- 16. • Guide the comprehensive response to HIV in Iringa (MOHSW, USAID and Partners) • Identify gaps in coverage (Ludewa District) • Advocate for additional services (Mufindi District) How Were the Data Used?
- 19. “I used coverage maps to argue that we needed to add coverage of CTC sites in the areas where no CTC existed, funding was allocated for four existing health facilities to begin offering care and treatment sites.” “Maps provide comprehensive visual data which is easy to access and interpret.”
- 20. Two ways we can encourage the use of local data: Better data are local data… …that are used for local decisions • Participatory data collection efforts • Provision of local data to local actors in accessible formats—such as maps
- 21. District, community and facility staff who provided support during data collection Collaborative effort •PLACE team based at UNC Chapel Hill •Futures Group Tanzania field office •GIS team at John Snow, Inc. Acknowledgements
- 22. MEASURE Evaluation is funded by the U.S. Agency for International Development (USAID) under terms of Cooperative Agreement AID-OAA-L-14- 00004 and implemented by the Carolina Population Center, University of North Carolina at Chapel Hill in partnership with Futures Group, ICF International, John Snow, Inc., Management Sciences for Health, and Tulane University. The views expressed in this presentation do not necessarily reflect the views of USAID or the United States government. http://measureevaluation.com/
Editor's Notes
- In this presentation, we are going to look at two MEASURE Evaluation activities in Iringa Region, Tanzania, which were designed help local actors understand local HIV prevention activities, as well as existing HIV/AIDS service coverage, in preparation for an expansion in comprehensive HIV prevention and treatment services.
- This morning, Dr Rosling started off his presentation with the thesis better data means local data. I will (quite humbly), expand that statement a little to say “better data arelocal data that are used for local decisions”. In the following slides we will review the local data collected during the PLACE Lite study and the Iringa Participatory Mapping Exercise, and share illustrative examples on how the data have been used.
- But first a little context. Iringa and Njombe Regions are located on the southern border of Tanzania, and have among the highest HIV prevalence in the area.
- Together, they have a population of 1.6 million persons. The two largest population centers are Iringa and Njombe towns here and here.
- Due to the high HIV prevalence, the USAID-funded MEASURE Evaluation project was asked to conduct two data gathering and data use initiatives, in preparation for scale-up of biomedical and general prevention activities
- In the first activity, a Priority for Local AIDS Control Efforts, or PLACE study was conducted. In this study, quantiative and qualitative data on HIV transmission dynamics, including the locations of hotspots and demographics of patrons attending those hotspots, are collected.
- This information gets summarized for each district and shared with local health authorities through reports
- And maps
- In the second activity, MEASURE Evaluation piloted a novel, low-cost approach using easily replicable methods to identify catchment areas and estimate coverage patterns for facility-based and outreach HIV services. The approach used combination of key informant interviews, printed maps, and open source GIS software to produce computer-generated, district-level maps of catchment and coverage patterns.
- The Iringa Participatory Mapping Exercise involved collecting GPS coordinates for all facilities and HQs of community groups providing HIV/AIDS services in the region, working with facility managers or community group leaders to identify catchment areas for services, digitizing those service areas, and … leveraging existing demographic data and service statistics to estimate service coverage in these catchment areas.
- … leveraging existing demographic data and service statistics to estimate service coverage in these catchment areas. Services examined included facility based and community based services.
- So, how did all this data get shared, and who were they shared with? The primary audiences for the data were the USAID mission in Tanzania, the Ministry of Health and Social Welfare, and Community based HIV prevention organizations. The data were shared through reports (show report), through online maps (online maps), and through a workshop setting (workshop).
- Identifying potential gaps in coverage: At the regional workshop, the Njombe regional medical officer and the Ludewa district council health management team identified a gap in care and treatment centers near the center of the district in an area with increased mining activity and high population growth.
- Advocating for additional services: DHMT members in Mufindi noted that the existing care and treatment centers provided insufficient coverage and included over-burdened facilities. With this and other information, they lobbied for and obtained funding to offer care and treatment at additional facilities.
- Service coverage maps were well received by district and regional health authorities. District and regional staff returned to their offices from the workshop and hung the maps in clear view, where they were used for planning supportive supervision visits and identifying gaps in service for the annual planning process. One participant said “quote 1”. Another, “quote 2”. We’ve mentioned how Mufundi and Ludewa districts used the maps. Another district used the HIV maps to illustrate the power of geographic data to leverage funding from donors for mapping environmental and sanitation issues. So, to wrap it all up and bring it all together: We used highly participatory data collection methods, which kept costs to a minimum, and encouraged local actors to think about the data as it was collected. We combined existing data-set both routine health statistics and population estimates, with digitized maps to generate local coverage estimates. We involved decision makers at multiple levels The maps were well received—one stakeholder said “…” Another stated: “…” So we started with the question, “what does better data mean” I would answer: better data are data which are used. And I would further answer: Two ways we can encourage data use are participatory data collection efforts, and provision of information in clear, readily accessed formats—such as maps.
- We started with the question, “what does better data mean” I would answer: better data are data which are used at local levels to improve health services. And I would further answer: Two ways we can encourage use of local data are participatory data collection efforts, and provision of information in clear, readily accessed formats—such as maps.