We are all acutely aware of the global health context and the immediate need for effective, efficient health policies, strategies and service interventions. Health issues have become increasingly complex necessitating quicker access to more and better data – to inform health decision makingHowever, the reality in many of the contexts where we work – this isn’t happening as often as it should Decision making often isn’t informed by existing data.
Why is this? Why are we experiencing a research to practice gap?A myriad of reasons inhibit data informed decision making. One which is at the root of the problem is the – the weak link between research, program and policy processes.These processes are distinct, often function independently and are characterized by specific ideologies, norms and values.The research process is defined as controlled, empirical, objective and time consuming.On the other hand, the program process is based on practical solutions, urgency and action. Populations are waiting to be served.Whereas, the policy process is characterized by a bargaining, lobbying, accommodation and compromise.Ideally, research, policy and practice are mutually dependent partners to support advances in public health. In reality however, they do not easily work together because of their different and sometimes conflicting norms and values. The lack of interaction between the disciplines often translates into poor understanding of the research process and the data they generate, an insufficient ownership of data and ultimately, low use of the data in decision making.
The question we need to ask ourselves is - What can be done to bridge the research to practice gap to improve use of data in decision making?A critical first step, is to expand the traditional research approach – As researchers we canFirstly,identify the program and/orpolicy improvement that the research is supporting. By identifyingthe programmatic and policy decisions that the research may influence we identify the primary consumers of the data. This allows us to respond directly to their information needs.Secondly, we can involve the data users - in the research process. By involving key program and policy stakeholders meaningfully we increase understanding of the research process. This heightened level of understanding translates into the resulting data being trusted by those that will use them.When data users trust the data collection process and resulting data they are more likely to make that data more available and accessible to others in their contexts. This forms the building blocks of data stewardship among the data users.And lastly, we need to challenge ourselves as researchers to go beyond study dissemination and ensure that the data we collect is linked to decision making processes. So, how does all of this play out in an actual research context?
Let’s look at an example of where these recommendations were applied in MadagascarThe MOH in Madagascar was committed to reduce unmet contraceptive need among remote, rural populationsThey decided to experiment with using community based workers to provide DMPAThey trained 62 community-based workers from 13 rural communities to provide DMPATALK about how building ownership of the research process was particularly important in Mada because of the controversy surrounding the intervention.
A study was designed around this pilot intervention to determine the safety, feasibility & acceptability of CBW distribution of injectable contraception.A single round of data collection approximately 7 months after intervention implementation. Trained interviewers conducted structured interviews with all CBD workers who initiated service delivery following training, as well as their supervisors attached to the public sector health center and the supporting NGO.They also interviewed five clients per CBD worker, and reviewed CBD workers’ administrative records reflecting matters like inventory management and accounting.All workers provided DMPA according to set safety standards, lay provision of DMPA was acceptable to workers, their clinical supervisors and clients, and that the provision of the new service attracted 41% new family planning users. The data were used by the Ministry of Health to scale-up the program from 13 to 27 districts in 2008 and to 68 districts today.Acceptance this rather controversial approach was an important moment in FP provision not just in Madagascar but in the world. Many countries would not even consider what seemed to be such a risky intervention.How did Madagascar succeed? How did study results get adopted and scaled up so readily?
To describe what we did to enhance the research process, I have broken out the steps in the research process as you see on the slide. I’ll discuss what we added to each step to improve the linkage between with the research and program and policy processes.
Let’s look at the first step. Study question development. Here we identified our target data user. We knew that if the study proved successful, we’d need to change clinical policies to guide the implementation of the new intervention. With this in mind we selected members for a steering committee to guide the research. This steering committee informed us of the specific data they’d need to do so.Next in the protocol development phase we conducted a thorough analysis of our secondary stakeholders. There were the individuals that would be affected by the change in policy. These people included not only clinical providers but also the professional medical associations that were not supportive of giving clinical responsibilities to lay health workers with limited medical training. We also identified community leaders as stakeholders because they were in a position to facilitate the uptake of the intervention in their communities – assuming positive study results. Also during the protocol development phase we identified meaningful involvement in the study process for some of our key stakeholders. For example we had high level decision makers in the MOH as co-investigators on the study responsible for drafting sections of the protocol. In the data collection phase we actively involved key stakeholders in the data collection process - such as district and national -level MOH staff Seeing the data collection process up close and participating actual data collection helped to increase study understanding and trust in the data being collected.
During the data analysis phase we involved key stakeholders in interpreting the data. We asked for their input to contextualize the result. This discussion provided rich local context to the findings. This active participation of key data users in interpreting the results clarified any remaining confusion or questions about the data and build ownership of the results.During the recommendation development process, local stakeholders took the lead of this process. Their understanding of the local context and allowed them to develop feasible and actionable And lastly, during the dissemination process we implemented the communication plan that had been developed earlier in the study that allowed us to tailor our dissemination activities and target specific findings and recommendations to different audiences in formats that were appropriate, relevant and accessible to them.
To conclude, the enhancements that we put in place to strengthen the research process contributed to an overall increase in the ownership of the study data by:Focusing on the data users information needs we generated the data needed for decision makingBy meaningfully involving the data users in the research, understanding of the research process and the data it generated was improvedThis led to an increased buy-in to move the recommendations into programmatic and policy changeAnd finally –contraceptive use among women in remote, rural areas of Madagascar was increased.
Conducting High Impact Research: Building data ownership and improving data use
Conducting High Impact Research: Building data ownership and improving data use <br />Tara Nutley, Theresa Hoke, Scott Moreland<br />Global Health Metrics & Evaluation Conference<br />March, 15 2011<br />
Global Health Context<br />Need to develop data-informed health policies, strategies and interventions<br />
Barriers to Data-informed Decision making<br />Weak link between research, program & policy processes<br />Different ideologies, norms & values<br />Research – controlled, empirical, objective<br />Program – practical, urgency, action<br />Policy – bargaining, lobbying, compromise<br />Low understanding, ownership and use of data<br />
Strengthening Data-informed Decision Making <br />Improve the research process<br />Consider the program/policy context in planning phase <br />Involve stakeholders throughout the research process<br />Make data and results available & accessible <br />Move beyond dissemination<br />
Improving Access to Injectable Contraception in Madagascar<br />Reduce unmet contraceptive need <br />2007 pilot program to integrate <br /> injectable services into <br /> community-based family planning<br /> distribution<br />62 community-based workers trained to offer injectables<br />
Can injectable contraception be safely provided by community workers?<br />Data collection<br />7 months post intervention - 61 CBWs, 25 supervisors and 303 clients interviewed<br />Results<br />Safe, acceptable, feasable<br />41% of acceptors - new family planning users<br />Program scaled up from 4 to 68 districts<br />
Steps <br /> - Research Process<br />Enhancements<br />Value Added<br />ID target data user<br />Steering committee<br />Targeted priority information needs<br /> Question development<br />ID other stakeholders<br />Roles / responsibilities<br /> Protocol development<br />Ensured involvement and buy-in<br />Stakeholders participated in data collection<br /> Data collection<br />Increased study understanding<br />
Steps<br />Enhancements<br />Value Added<br />Data interpretation<br />Local program context<br />included<br /> Data Analysis<br />Data use action plan<br /> Recommendations<br />Feasible & actionable recommendations developed<br />Communication plan<br /> Dissemination<br />Results targeted to audiences<br />
Strengthened Research Process<br />Increased ownership of data<br />Generated useful, priority data<br />Improved understanding of study process & data<br />Increased buy-in for recommendations<br />Increased data use<br />Improved health programs<br />
Discussion Question<br />Can a similar process be applied to multi-country research questions? Research questions that rely on secondary analysis?<br />How can we ensure collaboration between data users and data producers without delaying and increasing the complexity of already complex processes. <br />
Thank You <br />MEASURE Evaluation is funded by the U.S. Agency for <br />International Development and is implemented by the<br />Carolina Population Center at the University of North <br />Carolina at Chapel Hill in partnership with Futures Group<br />International, ICF Macro, John Snow, Inc., Management <br />Sciences for Health, and Tulane University. The views <br />expressed in this presentation do not necessarily reflect<br />the views of USAID or the United States Government.<br />