Using Goals To Assist In Strategic Planning Jan 09 V4

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  • Thank you Nicole. And thank you everyone for coming and sharing ideas on how to best use data to improve the impact of our policies and programs. I’ve been asked to talk about the use of models in terms of informing decision making. Specifically I will be talking about the Goals model. Tobi, alluded to the application of the Goals model in Viet Nam. Goals has been applied in several countries. In Asia, we have often combined Goals and the AEM model (Asia Epidemic model) for the analysis. In African countries, we have used the full Goals model to help inform resource planning. The example I am going to talk about today is from Indonesia, but the process is essentially the same in all of our applications. I happen to be most familiar with our Asian applications.


  • 1. Goals Model for Resource Planning and Allocation for HIV and AIDS Programming Nalinee Sangrujee, PhD MPH Futures Group International From Data to Impact: Using Health Data for Results Arusha Tanzania, 29 January 2009
  • 2. What we are doing in Indonesia
    • Building Capacity to Synthesize Data to Support and Inform National Strategic Planning and Resource Allocation Decisions for HIV/AIDS in Indonesia
    Building Capacity to Synthesize Data to Support and Inform National Strategic Planning and Resource Allocation Decisions for HIV/AIDS in Indonesia
  • 3. In December 2006 received a request to assist in “Costing” the National HIV/AIDS Action Plan
    • In 2006, National AIDS Commission was re-established and a national action plan was drafted
    • No link between strategy, implementation strategy and resources
    • Due to decentralized political structure, need to engage subnational level to mobilization resources for HIV/AIDS programs
    • HPI brought in to conduct a cost analysis and develop tools so support planning of HIV/AIDS programs
    Activities Goals Budgets Objectives Budgets are not linked to goals!
  • 4. GOALS: using available data to inform program and policymaking
    • Links policymaking and program planning with data analysis
    • Provides a deeper understanding of programs through critical review of data
    • Explicit prioritization of decisions and data gaps
    • Strengthens links of M&E systems to planning
  • 5. Surveillance Data Epi Models (EPP, AEM, Goals) Population Estimates Model epidemic patterns Spectrum Cost and coverage data Existing effectiveness data Goals How does all this data fit together?
  • 6. Stakeholder meeting Adaptation of GOALS Model (review HIV/AIDS strategy and program) Training and model set up Draft report Presentation of preliminary results Dissemination Senior Policy Symposium Implementation of the GOALS Model
    • Timeframe: from 9 months to 18 months
    • depends on available data
    • cooperation of partners
    • vetting process required to review the data, model, and results
    Data Collection Data Consultation Data Analysis
  • 7. Goals is intended to be a tool to assist interactive discussions among all stakeholder
    • HIV/AIDS Steering Committee
      • National AIDS Committee (KPA), MOH, UNAIDS, USAID, AusAid, Civil Society, etc.
    • National Modeling Team
      • National AIDS Committee, University, MOH, Ministry of Family Planning, Ministry of Social Welfare, Ministry of Planning, UN, NGOs, etc.
    • 11 Ministries
      • Social Welfare, Family Planning, Justice, Security, Tourism, Religious Affairs, Women’s Affairs, Health, etc.
  • 8. Use Goals to explore “What If” questions in order to analyze different resource allocation strategies
    • Between Interventions
    • Geographically
    • Temporally
    • Where should countries focus their efforts to yield the greatest impact
    • How much of an impact would be feasible in the next few years given the resources available?
    Strategic plan Impact Scenario 2 Scenario 3
  • 9. Two Years Later….
    • Revised National Action Plan
      • More specific in its implementation and targets
    • Ownership and investment of the application at the provincial level
      • Promoted as national methodology for HIV/AIDS strategic planning
    • Data has been used in global fund applications
    • National Cadre of trainers and master applicators
      • 42 people trained at the national level in the models and as trainers
      • 66 people in 33 provinces trained in data analysis and advocacy
  • 10. Models help to…
    • Support a comprehensive program approach
    • Breakaway from budget based planning
    • Support Advocacy efforts
    • Models stimulate greater discussion
      • Priorities
      • Identify data gaps
    • Improves understanding of interactions, but not provide the “optimum” answer
  • 11. Implementing GOALS at the country level
    • Process matters …
      • … because it determines the demand for
        • epidemiology data
        • financial and human resource data
        • impact data
        • M&E systems that link to decision making
    • Parallel processes:
      • Training and capacity building
      • Participatory Data Estimation
  • 12. Challenges go beyond the technical issues
    • Data estimation
      • Technical working groups, sharing experiences/data from other countries
    • Gap in dialogue between technical experts, policy makers, program implementers
      • Consultative meetings
      • Trainings on the models and the analysis process on evidence based decision making
  • 13. Challenges go beyond the technical issues
    • Organizational challenges
      • competing priorities, agency self interest, capacity
      • Action: numerous stakeholder meetings, explicit role of model to inform national policy
    • Budgeting approach mindset
      • Training in estimating costs and resource needs
      • Advocacy training to empower organizations/individuals
  • 14. Factors that facilitated data use
    • Policy champion
    • Multisectoral approach
    • Translation of Goals
    • Use of M&E data
    • Engagement data users in data collection
    • Advocacy training
    • Easy to digest output and policy briefs
  • 15. Keys to replication and sustainability
    • Identify the policy question
      • Drives the data needs
    • Repeated trainings
      • Training of trainers
      • Hands on trainings
    • Follow up with participants of trainings
      • how will new skills be used
      • how they are going to build capacity in country
    • Open sharing of data
    • Engagement of all stakeholders
    • Partnership
  • 16. Acknowledgements
    • Indonesia National AIDS Committee (KPA)
      • Ibu Nafsiah
      • Pak Kemal
    • Ministry of Health Indonesia
    • USAID
    • FHI
    • UNAIDS
    • University of Indonesia
    • KPA National Costing Team
  • 17. Thank You