Enhancing FP/RH Decision Making through GIS Data Linking

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A MEASURE Evaluation PRH webinar by James Stewart

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Enhancing FP/RH Decision Making through GIS Data Linking

  1. 1. GIS Data Linking toEnhance Multi-sectoral Decision Making forFamily Planning and Reproductive Health:A Case Study in RwandaJames StewartMEASURE Evaluation PRHMay 16, 2013
  2. 2. Organization of the Webinar Speaker Information Acknowledgements Introduction GIS data linking considerations for multi-sectoral and/ormulti-program data Examples of GIS linking, visualization, and analysisbased on data for Rwanda Lessons learned Question and answer session2
  3. 3. Speaker Information James Stewart Geographer / SeniorSpatial Analyst withMEASURE Evaluation 15 years of experienceas a GIS professional j.stewart@unc.edu3
  4. 4. Acknowledgements Based on their significant contributions to thedevelopment of the case study, special thanks areextended to the following individuals: Dr. Fidel Ngabo, Director of Maternal and Child Health(MCH), Rwanda Ministry of Health (MOH). Dr. Charles Ntare, Head of Integrated HealthManagement Information Systems/HMIS, RwandaMOH.4
  5. 5. Acknowledgements (continued) Mr. Randy Wilson, Senior Advisor, Health InformationSystems and Data Use, Management Sciences forHealth. Mr. Norbert-Aimé Péhé, Country Director, USAID |DELIVER PROJECT, John Snow, Inc. Mr. Max Kabalisa, Mr. Jovith Ndahinyuka, and Mr.Charles Nzumatuma, also of the USAID | DELIVERPROJECT in Rwanda.5
  6. 6. Acknowledgements (continued) MEASURE Evaluation PRH also extends sincereappreciation to everyone in Rwanda who participatedin or facilitated stakeholder interviews conducted inSeptember 2011. Organizations represented: MCH and HMIS units at the MOH USAID Monitoring & Evaluation Management Services(MEMS) Project MEASURE Evaluation6
  7. 7. INTRODUCTION7
  8. 8. Value of Linking Multi-sectoralData using a GISFamily planning andreproductive health(FP/RH) services helpprovide the foundationfor a healthy, stable,and economicallyviable society.Kigali, Rwanda, Sep. 20118
  9. 9. Value of Linking Multi-sectoralData using a GIS Past global strategies have often led to theimplementation of FP/RH programs that operate inisolation, despite the value of integrated approaches. The effectiveness of FP/RH decision making can beundermined by a lack of information from othersectors (e.g., education or food security), or fromother health areas (e.g., MCH or HIV/AIDS).OVC FP/RH HIVEDU AGRICULTUREPTMCT TBFOODSECURITYTRANSPORT POVERTY9
  10. 10. Value of Linking Multi-sectoralData using a GISOVC FP/RH HIVEDU AGRICULTUREPTMCT TBFOODSECURITYTRANSPORT POVERTYGlobal Health Initiative (GHI) principle number fiveemphasizes the need for strategic coordination andintegration to increase the impact of health programs.“The integration of health sector activities and theintegration of health sector activities with activities inother sectors – such as water and sanitation, education,food security, agriculture, economic growth, microfinance,and democracy and governance – can potentially achievehigh-yield gains for health.”Source: www.ghi.gov, May 2013.10
  11. 11. Value of Linking Multi-sectoralData using a GIS Multi-sector or multi-program integration can befacilitated by linking data sources. Linking multi-sectoral data sources is often deterredby information systems that are developed andmaintained independently of one another, leading todatasets that are unconnected or ‘stovepiped.’OVC FP HIVEDU AGRICULTUREPTMCT TBFOODSECURITYTRANSPORT POVERTY11
  12. 12. Value of Linking Multi-sectoralData using a GISThrough its ability tolink data usingcommon geographicidentifiers, ageographicinformation system(GIS) can helpovercome this‘stovepiping’ of data.12
  13. 13. Value of Linking Multi-sectoralData using a GIS After multi-sectoral linkshave been established, aGIS can Enhance visualizationand analysis of FP/RHprogram data. Make program datamuch easier tounderstand and to usefor evidence-baseddecision making.13
  14. 14. Value of Linking Multi-sectoralData using a GISMany benefits: Provides maps, which are highly visual tools. Establishes a more comprehensive foundation fordecision making. Increases data demand and use. Helps identify data quality issues. Supplies shared knowledge base for stakeholdercooperation. Facilitates better targeting of interventions.14
  15. 15. Value of Linking Multi-sectoralData using a GISFacilitates answers to geography-based questions: Do areas with a higher modern contraceptive prevalencerate (MCPR) exhibit lower HIV prevalence among womenof reproductive age in union? Is unmet need for FP different in urban and rural areas?15
  16. 16.  To explore these benefits, MEASURE EvaluationPRH sponsored a case study in Rwanda (fall 2011). Rwanda was selected as a case study for twoprimary reasons:1. Designated by the USAID Office of PRH as a prioritycountry for the support of FP/RH programming.2. Possesses a national spatial data infrastructure(NSDI) that is mature enough to facilitate GIS datalinking and analysis.Case Study in Rwanda16
  17. 17. Case Studyin Rwanda Goal was to explore datalinking opportunities usingfree and open source GISsolutions. Available in thepublications section of theMEASURE Evaluationsite.www.measureevaluation.org/publications/sr-12-7417
  18. 18. Goals of the WebinarBased on the Rwanda experience: Highlight the value of common geographic identifiers inkey data sources. Identify free and open source software (FOSS) solutionsfor GIS data linking, visualization, and analysis. Show how these GIS solutions can be used with multi-sectoral and/or multi-program data to enhance evidence-based decision making. Provide lessons learned to help accelerate the effectiveuse of multi-sectoral GIS data linking.18
  19. 19. GIS DATA LINKINGCONSIDERATIONS19
  20. 20. Key Data Sources Field visit in 2011 focused on exploring data linkingopportunities to provide useful examples. Some key data sources could not be accessed forGIS linking during the field visit because of theirconfidential or sensitive nature (e.g., PBF, TRACnet). Others could not be accessed because of timing ofvisit (e.g., HMIS, SISCom).20
  21. 21.  In this context, focused on data sources that had ahigher likelihood of being available in other countries. Primary data sources and sectors represented: Rwanda Demographic and Health Survey 2010:FP/RH, HIV, education, and nutrition. USAID | DELIVER PROJECT: FP (commodities). National Agricultural Survey, 2008: food security. Poverty Household Surveys for 2000 to 2011: poverty.Key Data Sources21
  22. 22. Common Geographic Identifiersfor Rwanda Primary considerationfor data linking. Linked key datasources usingcrosswalk.22
  23. 23. GIS Options ExploredFocused on free and open source software (FOSS)solutions to complement existing tools: Excel to Google Earth (E2G) Single indicator maps using Excel. Quantum GIS (QGIS) Multi-indicator and publication-quality maps as well asadvanced GIS analysis to extend functionality of DHIS 2. OpenGeoDa Simple but effective exploratory spatial data analysis (ESDA)using data in shapefile format.23
  24. 24. Excel to Google Earth (E2G) Quick and simple programfrom MEASURE Evaluation. Color-shaded (choropleth)map of a single variablewithout a GIS. Displayed on Google Earth’srich base map. Useful for data qualitychecks and illustratingreports. Good option for non-GISspecialists working in Excel.24www.measureevaluation.org/e2g
  25. 25. Quantum GIS (QGIS) Fully functional GIS. Excellent for multi-sectoral GIS data linking,visualization, andanalysis. Publication-quality maps. Perform advanced GISanalysis.25www.qgis.org
  26. 26. OpenGeoDaPercent Married Women Age 15–49using Any Method of ContraceptionData Source: Rwanda DHS 2010, Table D.32.26geodacenter.asu.edu
  27. 27. EXAMPLES OF GIS LINKINGAND ANALYSIS FOR RWANDA27
  28. 28. 28
  29. 29. 29
  30. 30. Comparing the TwoNo discernible correlation between general use ofcontraception, which includes both traditional and modernmethods, and HIV prevalence. No spatial overlap between districts with highest % of women usingcontraception and districts in Kigali with highest HIV prevalence. Districts with lowest contraception use do not appear to coincidewith either a lower or higher HIV prevalence.30
  31. 31. QGIS: Contraception Use vs. HIVPrevalence31
  32. 32. QGIS: Contraception Use vs. HIVPrevalence32
  33. 33. QGIS: Contraception Use vs. HIVPrevalence33
  34. 34. QGIS: Linking FP, Education, andPoverty Data34
  35. 35. QGIS: Linking FP, Nutrition, andFood Security Data35
  36. 36. Linking FP/RH Program Data withFP Commodities DataExample: Women using Any Modern Method ofContraception (MCPR) versus Couple Years ofProtection (CYP) Integrating FP commodities data from USAID | DELIVERPROJECT represents significant data linking opportunityfor many FP/RH programs. Relies on same data linking principles used in previoussections. This example can be used as a model for integratingUSAID | DELIVER PROJECT data into an existing HMIS.36
  37. 37. Linking FP/RH Program Data withFP Commodities Data Used district-level geographic identifiers for linking. Summarized CYP by district using Supply Chain Manager(SCM) data. CYP calculated relatively easily using routinely collecteddata and CYP conversion factors from USAID. CYP data need to be adjusted for unreported data and inventorybalance errors.37
  38. 38. Linking FP/RH Program Data withFP Commodities Data CYP is simple indicator of volume of FP commoditiesdistribution for a given geographic area. As simple sum of estimated contraceptive methoddurations: Does not take into account differences in sizes of reportedareas or underlying populations. Unsuitable for choropleth mapping. First necessary to normalize calculations based onproportion of district populations corresponding to womenof reproductive age.38
  39. 39. QGIS: Linking FP/RH Data with theUSAID | DELIVER PROJECT39
  40. 40. Map of MCPR vs. CYP Highlights ability of multi-program data linking touncover unexpected patterns and relationships. Shows how linking indicators in a single map canhelp target geographic areas for potentialinterventions. Illustrates the usefulness of GIS data linking forconducting cross-database comparisons.40
  41. 41. LESSONS LEARNED41
  42. 42. Lessons LearnedThree categories: Key data sources Common geographic identifiers Software42
  43. 43. Lessons Learned:Key Data Sources Some datasets are easily accessed and are at anappropriate geographic scale for analysis (e.g.,DHS). Others may be difficult to access and use for avariety of potential reasons, such as Data confidentiality concerns; Organizational barriers to data sharing; Geographic scale issues; and Timing of data access request.43
  44. 44. Lessons Learned:Key Data SourcesTo overcome data access limitations, recommendsetting up local stakeholder meetings to Discuss data linking benefits. Identify opportunities to collaborate. Develop an action plan. Establish long-term working relationships.44
  45. 45. Lessons Learned:Key Data SourcesSupply Chain Manager (SCM) database from theUSAID | DELIVER PROJECT: Need to work closely with USAID | DELIVER PROJECTstaff to obtain data adjusted to 100% reporting rate. Highly important to have accurate population data fornormalizing CYP. USAID | DELIVER PROJECT a key partner for GIS datalinking.45
  46. 46. Lessons Learned:Key Data SourcesHIV/AIDS Data Management System: FP/RH programs could benefit from linking HMIS to non-identifiable, aggregated data from HIV/AIDS system. Example based on stakeholder interviews: Could facilitate a more rapid response to such question as,“Is there an uptake of FP and HIV testing referralsassociated with integration of FP/RH and HIV services?”46
  47. 47. Lessons Learned:Key Data SourcesPerformance-Based Financing (PBF) System: Linkage of HMIS and PBF data could provide a cross-check of common indicators. Such a national-level data display and feedbackmechanism could provide strong incentive for healthcenters to perform.47
  48. 48. Lessons Learned:Common Geographic IdentifiersExcellent availability of geographic data and identifiersfor Rwanda: Could be downloaded from the NISR or MOH sites. Provides a good model for other countries to follow.48
  49. 49. Lessons Learned: Software Free and open source GIS software options haveadvanced in recent years: E2G and Google Earth are accessible to non-GISspecialists. QGIS offers high degree of functionality. GeoDa provides point-and-click geographic datavisualization and analysis. These solutions can complement existing systems.49
  50. 50. Summary and Conclusions Multi-sectoral/multi-program integration offers manybenefits and is a GHI priority. Multi-sectoral/multi-program integration can be facilitatedby GIS data linking, which requires common geographicidentifiers. Free and open source GIS solutions can meet many datalinking needs of FP/RH programs. The Rwanda case study can serve as a reference for howto apply multi-sectoral GIS data linking to enhance FP/RHdecision making.50
  51. 51. Thank you.Questions?www.measureevaluation.org/prh51MEASURE Evaluation Populationand Reproductive Health (PRH) isfunded by the U.S. Agency forInternational Development (USAID)through cooperative agreementassociate award number GPO-A-00-09-00003-00 and is implemented bythe Carolina Population Center atthe University of North Carolina atChapel Hill, in partnership withFutures Group, ManagementSciences for Health, and TulaneUniversity. The opinions expressedare those of the authors and do notnecessarily reflect the views ofUSAID or the U.S. government.

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