Use It or Lose It: How Avahan Used Data to Shape its HIV Prevention Efforts in India

This 44-page publication discusses the centrality of collecting and using data to inform the shape and evolution of Avahan, an HIV prevention programme launched in 2003 by the Bill and Melinda Gates Foundation in 6 of India's states. The document offers:

1. A brief overview of the different types of data generation activities and sources - Amongst the methodologies discussed are: size estimations of populations at risk, pictorial mapping tools, routine programme monitoring data (e.g., individual interactions between over 7,000 peer educators and their charges), lead-implementing-partner-led survey activities, cross-sectional surveys, general population surveys, "knowledge building partner" data, quality measurements (indicated by clinic performance, sexually transmitted infection (STI) case management, and clinic operations), and data from sources external to Avahan (e.g., National Family Health Survey)




2. An account of how data use by different constituencies in the programme has guided programme decisions and activities in areas such as resource allocation, implementation scale-up, course corrections and shifts in implementation, programme redesign, impact evaluation, and advocacy. For instance, within this portion of the documents several case studies are included that illustrate how monitoring data analysis was used to increase emphasis on promotion of clinical services by peer educators, the use of data to increase the involvement of peer educators in service delivery (mainly, outreach and condom distribution), and how analysis of clinic traffic data guided a redesign of the clinic service delivery model to promote repeat service uptake.
   
   

   Example (excerpt): "Greater peer ownership of outreach is accomplished through a process known as micro-planning, which is meant to help peer educators track individuals in their outreach networks to target the most at-risk and least reached members of the community with appropriate services....Peer educators carry individual tracking cards which allow them to capture the content of their interactions with the members of their outreach network. They then review these records, typically on a weekly basis, either meeting singly with their supervisor or by meeting and consulting as a group, and then take decisions on next week's "beat" or next steps for specific individuals in their network. Supervisors use these meetings and follow-up field visits to assess the quality and timeliness of record-keeping....This system has maximized coverage and enhanced the depth of service utilization by the community members. Peers now know critical characteristics of individuals in their respective networks and customize their outreach plan accordingly....This emphasis on peer led individual tracking and data use for work planning has helped build the skills and confidence of peer educators, which prepares them to become articulate managers, coaches, and spokespersons for their communities..."




3. A summary of lessons learned related to data use in a large, diverse programme:
   
   • Data collection does not automatically lead to use - Avahan tried to foster a data use culture by: (1) devolving ownership for collection and use of data to those who could use it most effectively; (2) building and enabling paper and software tools for data capture, analysis, and integration (including for nonliterate populations); and (3) instituting formal forcing mechanisms for data use such as multi-level reviews.
   • Focus on changing and improving the focus of measurement to align it with the stage of implementation-aided effective programme management.
   • "Purely quantitative indicators may seem easier to collect and measure. However, numbers can often deflect attention from harder to measure, but equally essential program attributes
     that might be qualitative."

   
   

4. Ongoing measurement and data use challenges include: (1) Negotiating data ownership issues to ensure that all the data are used effectively; (2) Managing data analysis at scale; (3) Integrating and synthesising multiple data sources to form a coherent view; (4) Collecting reliable data for "soft" or "hard-to-measure" areas such as community mobilisation; and (5) Sustaining a data use culture in the long term.