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Enhancing FP/RH Decision Making through GIS Data Linking

  1. GIS Data Linking to Enhance Multi-sectoral Decision Making for Family Planning and Reproductive Health: A Case Study in Rwanda James Stewart MEASURE Evaluation PRH May 16, 2013
  2. Organization of the Webinar  Speaker Information  Acknowledgements  Introduction  GIS data linking considerations for multi-sectoral and/or multi-program data  Examples of GIS linking, visualization, and analysis based on data for Rwanda  Lessons learned  Question and answer session 2
  3. Speaker Information  James Stewart  Geographer / Senior Spatial Analyst with MEASURE Evaluation  15 years of experience as a GIS professional  3
  4. Acknowledgements  Based on their significant contributions to the development of the case study, special thanks are extended 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 Health Management Information Systems/HMIS, Rwanda MOH. 4
  5. Acknowledgements (continued)  Mr. Randy Wilson, Senior Advisor, Health Information Systems and Data Use, Management Sciences for Health.  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 | DELIVER PROJECT in Rwanda. 5
  6. Acknowledgements (continued)  MEASURE Evaluation PRH also extends sincere appreciation to everyone in Rwanda who participated in or facilitated stakeholder interviews conducted in September 2011.  Organizations represented:  MCH and HMIS units at the MOH  USAID Monitoring & Evaluation Management Services (MEMS) Project  MEASURE Evaluation 6
  8. Value of Linking Multi-sectoral Data using a GIS Family planning and reproductive health (FP/RH) services help provide the foundation for a healthy, stable, and economically viable society. Kigali, Rwanda, Sep. 2011 8
  9. Value of Linking Multi-sectoral Data using a GIS  Past global strategies have often led to the implementation of FP/RH programs that operate in isolation, despite the value of integrated approaches.  The effectiveness of FP/RH decision making can be undermined by a lack of information from other sectors (e.g., education or food security), or from other health areas (e.g., MCH or HIV/AIDS). OVC FP/RH HIVEDU AGRICULTURE PTMCT TB FOOD SECURITY TRANSPORT POVERTY 9
  10. Value of Linking Multi-sectoral Data using a GIS OVC FP/RH HIVEDU AGRICULTURE PTMCT TB FOOD SECURITY TRANSPORT POVERTY Global Health Initiative (GHI) principle number five emphasizes the need for strategic coordination and integration to increase the impact of health programs. “The integration of health sector activities and the integration of health sector activities with activities in other sectors – such as water and sanitation, education, food security, agriculture, economic growth, microfinance, and democracy and governance – can potentially achieve high-yield gains for health.” Source:, May 2013. 10
  11. Value of Linking Multi-sectoral Data using a GIS  Multi-sector or multi-program integration can be facilitated by linking data sources.  Linking multi-sectoral data sources is often deterred by information systems that are developed and maintained independently of one another, leading to datasets that are unconnected or ‘stovepiped.’ OVC FP HIVEDU AGRICULTURE PTMCT TB FOOD SECURITY TRANSPORT POVERTY 11
  12. Value of Linking Multi-sectoral Data using a GIS Through its ability to link data using common geographic identifiers, a geographic information system (GIS) can help overcome this ‘stovepiping’ of data. 12
  13. Value of Linking Multi-sectoral Data using a GIS  After multi-sectoral links have been established, a GIS can  Enhance visualization and analysis of FP/RH program data.  Make program data much easier to understand and to use for evidence-based decision making. 13
  14. Value of Linking Multi-sectoral Data using a GIS Many benefits:  Provides maps, which are highly visual tools.  Establishes a more comprehensive foundation for decision making.  Increases data demand and use.  Helps identify data quality issues.  Supplies shared knowledge base for stakeholder cooperation.  Facilitates better targeting of interventions. 14
  15. Value of Linking Multi-sectoral Data using a GIS Facilitates answers to geography-based questions:  Do areas with a higher modern contraceptive prevalence rate (MCPR) exhibit lower HIV prevalence among women of reproductive age in union?  Is unmet need for FP different in urban and rural areas? 15
  16.  To explore these benefits, MEASURE Evaluation PRH sponsored a case study in Rwanda (fall 2011).  Rwanda was selected as a case study for two primary reasons: 1. Designated by the USAID Office of PRH as a priority country for the support of FP/RH programming. 2. Possesses a national spatial data infrastructure (NSDI) that is mature enough to facilitate GIS data linking and analysis. Case Study in Rwanda 16
  17. Case Study in Rwanda  Goal was to explore data linking opportunities using free and open source GIS solutions.  Available in the publications section of the MEASURE Evaluation site. publications/sr-12-74 17
  18. Goals of the Webinar Based on the Rwanda experience:  Highlight the value of common geographic identifiers in key data sources.  Identify free and open source software (FOSS) solutions for 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 effective use of multi-sectoral GIS data linking. 18
  20. Key Data Sources  Field visit in 2011 focused on exploring data linking opportunities to provide useful examples.  Some key data sources could not be accessed for GIS linking during the field visit because of their confidential or sensitive nature (e.g., PBF, TRACnet).  Others could not be accessed because of timing of visit (e.g., HMIS, SISCom). 20
  21.  In this context, focused on data sources that had a higher 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 Sources 21
  22. Common Geographic Identifiers for Rwanda  Primary consideration for data linking.  Linked key data sources using crosswalk. 22
  23. GIS Options Explored Focused 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 as advanced GIS analysis to extend functionality of DHIS 2.  OpenGeoDa  Simple but effective exploratory spatial data analysis (ESDA) using data in shapefile format. 23
  24. Excel to Google Earth (E2G)  Quick and simple program from MEASURE Evaluation.  Color-shaded (choropleth) map of a single variable without a GIS.  Displayed on Google Earth’s rich base map.  Useful for data quality checks and illustrating reports.  Good option for non-GIS specialists working in Excel. 24
  25. Quantum GIS (QGIS)  Fully functional GIS.  Excellent for multi- sectoral GIS data linking, visualization, and analysis.  Publication-quality maps.  Perform advanced GIS analysis. 25
  26. OpenGeoDa Percent Married Women Age 15–49 using Any Method of Contraception Data Source: Rwanda DHS 2010, Table D.32. 26
  28. 28
  29. 29
  30. Comparing the Two No discernible correlation between general use of contraception, which includes both traditional and modern methods, and HIV prevalence.  No spatial overlap between districts with highest % of women using contraception and districts in Kigali with highest HIV prevalence.  Districts with lowest contraception use do not appear to coincide with either a lower or higher HIV prevalence. 30
  31. QGIS: Contraception Use vs. HIV Prevalence 31
  32. QGIS: Contraception Use vs. HIV Prevalence 32
  33. QGIS: Contraception Use vs. HIV Prevalence 33
  34. QGIS: Linking FP, Education, and Poverty Data 34
  35. QGIS: Linking FP, Nutrition, and Food Security Data 35
  36. Linking FP/RH Program Data with FP Commodities Data Example: Women using Any Modern Method of Contraception (MCPR) versus Couple Years of Protection (CYP)  Integrating FP commodities data from USAID | DELIVER PROJECT represents significant data linking opportunity for many FP/RH programs.  Relies on same data linking principles used in previous sections.  This example can be used as a model for integrating USAID | DELIVER PROJECT data into an existing HMIS. 36
  37. Linking FP/RH Program Data with FP 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 collected data and CYP conversion factors from USAID.  CYP data need to be adjusted for unreported data and inventory balance errors. 37
  38. Linking FP/RH Program Data with FP Commodities Data  CYP is simple indicator of volume of FP commodities distribution for a given geographic area.  As simple sum of estimated contraceptive method durations:  Does not take into account differences in sizes of reported areas or underlying populations.  Unsuitable for choropleth mapping.  First necessary to normalize calculations based on proportion of district populations corresponding to women of reproductive age. 38
  39. QGIS: Linking FP/RH Data with the USAID | DELIVER PROJECT 39
  40. Map of MCPR vs. CYP  Highlights ability of multi-program data linking to uncover unexpected patterns and relationships.  Shows how linking indicators in a single map can help target geographic areas for potential interventions.  Illustrates the usefulness of GIS data linking for conducting cross-database comparisons. 40
  42. Lessons Learned Three categories:  Key data sources  Common geographic identifiers  Software 42
  43. Lessons Learned: Key Data Sources  Some datasets are easily accessed and are at an appropriate geographic scale for analysis (e.g., DHS).  Others may be difficult to access and use for a variety of potential reasons, such as  Data confidentiality concerns;  Organizational barriers to data sharing;  Geographic scale issues; and  Timing of data access request. 43
  44. Lessons Learned: Key Data Sources To overcome data access limitations, recommend setting up local stakeholder meetings to  Discuss data linking benefits.  Identify opportunities to collaborate.  Develop an action plan.  Establish long-term working relationships. 44
  45. Lessons Learned: Key Data Sources Supply Chain Manager (SCM) database from the USAID | DELIVER PROJECT:  Need to work closely with USAID | DELIVER PROJECT staff to obtain data adjusted to 100% reporting rate.  Highly important to have accurate population data for normalizing CYP.  USAID | DELIVER PROJECT a key partner for GIS data linking. 45
  46. Lessons Learned: Key Data Sources HIV/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 referrals associated with integration of FP/RH and HIV services?” 46
  47. Lessons Learned: Key Data Sources Performance-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 feedback mechanism could provide strong incentive for health centers to perform. 47
  48. Lessons Learned: Common Geographic Identifiers Excellent availability of geographic data and identifiers for Rwanda:  Could be downloaded from the NISR or MOH sites.  Provides a good model for other countries to follow. 48
  49. Lessons Learned: Software  Free and open source GIS software options have advanced in recent years:  E2G and Google Earth are accessible to non-GIS specialists.  QGIS offers high degree of functionality.  GeoDa provides point-and-click geographic data visualization and analysis.  These solutions can complement existing systems. 49
  50. Summary and Conclusions  Multi-sectoral/multi-program integration offers many benefits and is a GHI priority.  Multi-sectoral/multi-program integration can be facilitated by GIS data linking, which requires common geographic identifiers.  Free and open source GIS solutions can meet many data linking needs of FP/RH programs.  The Rwanda case study can serve as a reference for how to apply multi-sectoral GIS data linking to enhance FP/RH decision making. 50
  51. Thank you. Questions? 51 MEASURE Evaluation Population and Reproductive Health (PRH) is funded by the U.S. Agency for International Development (USAID) through cooperative agreement associate award number GPO-A-00- 09-00003-00 and is implemented by the Carolina Population Center at the University of North Carolina at Chapel Hill, in partnership with Futures Group, Management Sciences for Health, and Tulane University. The opinions expressed are those of the authors and do not necessarily reflect the views of USAID or the U.S. government.