Monitor to innovate with feedback loops: process evaluation protocol for an anemia prevention intervention

Anemia endemic among women of reproductive age

Anemia affects roughly a third of the world's population, making it a major global public health problem that impacts maternal and child mortality, cognitive and physical performance¹, and work capacity and productivity^2,3. In India, more than 50% of women of reproductive age have anemia³. The largest contributor to anemia worldwide is iron deficiency⁴. Despite concentrated efforts and the presence of several government programs (e.g., National Iron Plus Initiative and Anemia Mukt Bharat)⁵, which offer women of reproductive age free iron supplements^6–9, anemia prevalence remains high³. India’s Demographic Health Data (2015 -16) shows that only 30% of pregnant women consumed iron folic acid for more than 100 days during pregnancy³.

Several studies have examined supply side issues to IFA consumption^10–13, but studies on demand side challenges including knowledge and social barriers are limited¹⁴. Recently, researchers have begun to ask questions about the role that community-level factors, such as social and gender norms, can play in women’s IFA consumption^15–18. The Reduction in Anemia Through Normative Innovations (RANI) project seeks to bridge this gap with a multilevel intervention focused on increasing demand for IFA by influencing anemia-related social norms¹⁹.

The RANI (Reduction in Anemia through Normative Innovations) project

The RANI project is a cluster randomized trial launched in 2019 in the Angul district of Odisha. Residents live in rural areas and primarily identify as Hindu²⁰. Like the rest of the country and the district of Odisha, India, almost half of all women of reproductive age are anemic¹⁴. In the study, there are 89 selected clusters of villages, which we randomized into treatment and control on a 1:1 basis. The usual-care control arm receives no intervention, whereas the treatment arm receives the RANI project components. In total, 15 clusters (40–41 villages) were selected and 4000 women (2000 in each arm) living in the selected clusters were randomly selected to take part in data collection.

The RANI Project’s overall implementation approach, based on its goal to improve IFA consumption through a social norms-based intervention, includes a number of intervention components (see Figure 1 for a visual description of each intervention component), which require monitoring and iterating the intervention delivery strategy at multiple ecological levels throughout the implementation period. Ethical approvals to conduct the RANI project have been acquired from appropriate institutions within the United States and in India (see Yilma et al. 2020 for the full intervention protocol¹⁹).

Figure 1. Visualization and description of RANI Project intervention components.

Process evaluation framework

In public health interventions, process evaluation allows researchers and practitioners to understand what works, what does not work, for whom, and under what conditions. It also serves the important function of providing ongoing feedback so that interventions can be responsive to the changing conditions in the field^21–23. Process evaluations also help determine whether fidelity to the implementation protocol was maintained during the course of the intervention period, whether intended participants were reached, and if any unintended consequences resulted from the project^24,25.

Health care delivery and outcomes can be improved by using innovations (i.e., new ideas, technologies, and practices) supported by scientific evidence. Over the last couple of decades, process evaluation methodologies have been evolving²¹. They were originally centered on qualitative research alongside trials and were conducted to provide a deeper understanding of the disease condition, implementation issues and mechanisms of the intervention²². Now, there is growing consensus that qualitative and quantitative data (mixed methods) can help facilitate trial implementation, identification of the effectiveness of “active ingredients”, and research translation. As methods have advanced, integration of feedback loops is emerging as the next methodological frontier for process evaluations.

Use of feedback loops

Feedback loops are mechanisms that allow intervention information to flow back to the program implementers on an ongoing basis so that appropriate changes in intervention delivery strategy can be adopted throughout the implementation period. Incorporating feedback loops into the process evaluation design ensures interventions are meaningfully and strategically responsive to emerging requirements. In modern times, these loops increasingly rely on mobile and other forms of digital technology, which improves accuracy and reduces the time lag between events on the ground and program adaptations. As such, feedback loops can improve decision-making throughout the intervention.

Historically, institutionalizing feedback loops as an integral part of process evaluations has proven to be challenging^26–29. Many scholars²⁹ have characterized the need for feedback loops “as a bold challenge to current orthodoxy aided by developments in theory, methods, and practice” (p. 3). They call for an approach that promotes interaction between project designers, implementers, researchers, and decision-makers to encourage adaptation through learning. Such an approach would center on agility, responsiveness, a culture of experimentation, responsiveness, and relevant, timely, actionable data as its key characteristics. Furthermore, scholars advocate moving away from current dominant models of static intervention design and implementation, which tend to be modeled after trials that do not permit projects to be responsive to the complexities and unpredictability of implementation challenges that arise in social and behavioral intervention trials²⁹.

The use of feedback loops is mainstream in the commercial sector for corporations such as Uber, eBay, and Airbnb^30–33. Similarly, there are several examples of improved implementation delivery and outcomes by leveraging feedback loops. They range from engaging millions of youths in informing policy, improving use of evidence in decision-making, improving flood response time, and efficiency in HIV treatment targeting in Zimbabwe^34–40. Citizen reports of drug stock-outs and improvement in community health worker performance^34,41–43 are other examples of the effective application of feedback loops^34,44–46.

In sum, the synergy between feedback loops integrated within process evaluations and enhancement of implementation delivery is still an under-explored area, especially in the context of randomized control trials (RCTs)⁴⁷. One of the monitoring goals of the RANI project was to reduce this knowledge and implementation gap. Specifically, we describe the process evaluation methods we are adopting in a multilevel intervention to increase iron folic acid supplement use for anemia prevention in Odisha, India.

Ethics approval and consent to participate

Approval to conduct the study was gained from The Institutional Review Board at The George Washington University (FWA00005945) and the Sigma Science and Research, an IRB located in New Delhi, India (10031/IRB/D/18-19). This trial was registered with Clinical Trial Registry- India (CTRI) (CTRI/2018/10/016186) on 29 October 2018. All participants went through a verbal and written informed consent process before data collection.

Process evaluation aims and questions

The RANI project process evaluation has three aims.

Our process evaluation questions are anchored in the RANI project’s process evaluation framework and aims. They were finalized in collaboration with our stakeholders (Table 1), who serve as key members of the feedback loop decision-making sub-team (i.e., principal investigator, project director, M&E (monitoring and evaluation) technical lead, intervention implementation and evaluation manager) within our project.

Following the finalization of our evaluation aims and questions, we developed a logic model and M&E planning framework (See Online Repository). This framework details key inputs, outputs, outcomes (short, intermediate, and long-term) and links them with data collection instruments. It outlines the frequency of data entry, responsible parties, reporting frequencies, data visualization tools, and a plan for disseminating our findings. Furthermore, the framework served as a guiding document when developing the wireframe and final design for our process evaluation software application, called the Real-time Performance Monitoring for Knowledge (RPM4K).

Real-Time Performance Monitoring for Knowledge (RPM4K)

RPM4K is a software application customized and developed locally for the RANI Project process evaluation. It facilitates intervention planning, delivery, performance monitoring, and automated production of data visualization and reports using near to real-time data to identify and inform feedback loops (Figure 2). RPM4K application offers a dual mobile application and a web-based interface.

Figure 2. RANI Project’s Process Evaluation Conceptual Framework.

The process evaluation data lifecycle centers and revolves around our community facilitators and their cluster supervisors. RANI community facilitators, who serve as frontline workers to deliver intervention components, use the system to enter field level data with the help of a hand-held (mobile) device. Their cluster supervisors use the mobile as well as the web interface for entering supervision data and also monitoring intervention delivery and quality by reviewing data entered by RANI community facilitators. Key stakeholders use the web interface to monitor the pace and delivery of intervention activities of both cluster supervisors and facilitators and utilize aggregated data to identify feedback loops and implement tweaks to the intervention. Access to RPM4K is tiered at multiple levels.

Community facilitators are only able to access their own performance data as well as intervention data for the villages they work in. Similarly, their cluster supervisors can only access performance data for the community facilitators they manage and the villages they work in. Key members of the process evaluation team (e.g., process evaluation lead, state implementation manager, project managers etc.) are able to access and view intervention data for all villages and performance data for all personnel. However, the system administrator is the only person with access to all structural functionalities of RPM4K. They are responsible for mapping intervention villages to cluster supervisors or community facilitators and defining data access for all users.

At the beginning of each month, the system administrator is responsible for uploading intervention plans onto RPM4K. They direct the RANI project’s process evaluation data lifecycle, quality control, and technical assistance requests from RPM4K end users. At month’s end, data on key performance indicators is auto aggregated into dashboards and reports in the form of easy to interpret heat maps and visual reports. Raw data for specific time periods or the entirety of the intervention delivery period can also be downloaded from RPM4K by users with defined data privileges.

Data collection

We collect, process, visualize, and triangulate data from 130 intervention villages in near to real-time using RPM4K. Data collected ranges from the individual level (e.g., community facilitator performance and hemoglobin levels) to the village-level (e.g., IFA supplement supply and demand). Key performance indicators for each ecological level are summarized in Table 2. A data quality assurance protocol is in place to review various levels and layers of data in a collaborative manner (See Supplementary Material). Data entry, management, review occurred in monthly cycles. Frontline community facilitators enter data in near-to-real time. Their supervisors conduct the first round of data review followed by a final check of data quality by the Data Manager prior to approving submitted data. Data quality review focused on conducting range checks for data values, monitoring and removing duplicate, blank or outlier values, timeliness with encoded deadlines for data entry submissions, completeness check against monthly implementation plans. All data was housed in a secure cloud server with full privileges limited to the Data Manager only.

Monthly qualitative summary report

RANI project community facilitators submit a monthly qualitative report summarizing their reflections, perceptions, and experiences regarding the quality of their intervention sessions, self-assessment of their performance, participant and community reactions to intervention activities, barriers and facilitators and unintended consequences, if any (See Online Repository for the full interview guide). All open-ended responses get captured in RPM4K in Odiya, the local language, and then get translated and deductively summarized by the Data Manager using the interview guide to formulate themes in a monthly report written in English using Microsoft Word.

Quantitative measures and instruments

The majority of quantitative data is collected and entered into RPM4K through the Community Facilitators Input Form (submitted by community facilitators), the Community Facilitators Evaluation Form (submitted by their cluster supervisors). These forms have dedicated sections dealing with key performance indicators for each intervention component (i.e., T4, Hemoglobin testing, RANI Comms, IFA supply and demand status, etc.). The forms also have built-in global positioning system (GPS) and activity image capture features to associate location and visual data with intervention activities. All forms are submitted within four days of completing an intervention activity. See Online Repository to review our data collection instruments and screenshots of RPM4K interface, dashboards, and data visualization products.

Dose

Dose is operationalized as the total number of intervention activities (e.g., T4 sessions, community engagement meetings, RANI Comm video demonstration sessions, hemoglobin testing sessions and results demonstrations) reported on RPM4K and summarized at the village-level by algorithms embedded within RPM4K.

Reach

Reach is measured as the total number of women of reproductive age, their families (i.e., husbands and mothers-in-law), frontline workers and policy makers in the village exposed to intervention activities. Reach is summarized at the village-level by algorithms embedded within RPM4K.

Receptivity

Receptivity to intervention components is operationalized as the total number of likes versus dislikes shared by program participants following the delivery of an intervention activity by RANI community facilitators. Receptivity is summarized at the village-level by algorithms embedded within RPM4K.

Community facilitator performance

Community facilitator performance is assessed and monitored using three distinct tools implemented at different timepoints. The first tool measures the baseline professional abilities of RANI community facilitators when they are first onboarded as part of the RANI project implementation team. Each RANI community facilitator is classified on a scale of 1 (exceptional) to 3 (needs improvement) points on six measures – 1) their prior work experience in participatory learning activities or community mobilization, 2) communication skills, 3) professional network, 4) familiarity with the intervention area, 5) references from prior colleagues or supervisors, 6) logistical preparedness related to job requirements.

The second tool for assessing and monitoring community facilitator performance is integrated within RPM4K. It consists of a self-assessment scale reported by community facilitators following every intervention session followed by monthly cluster supervisor assessment of community facilitators. The community facilitator self-assessment scale consists of three measures which ask 1) how easy or difficult it was to deliver the intervention session they’re reporting on, 2) how much the community facilitator thinks participants understood each session, and 3) how they would rate their overall performance for the session they just delivered. The response options consist of five-point Likert scales ranging from very difficult to very easy, did not understand at all to understood all of the content, and very poor to very good, respectively. Similarly, cluster supervisors conduct random visits covering at least 10% of all intervention sessions delivered by a community facilitator during any given month. They perform intervention session evaluations and report community facilitator performance by observing and assessing their pace, accuracy, confidence, and ability to hold the audience’s attention when delivering intervention components. The response options consist of five-point Likert scales ranging from too slow to too fast and inaccurate to accurate; three-point Likert scales ranging from not at all confident to very confident and not at all focused to very focused. Data from these two tools are summarized at the village-level by algorithms embedded within RPM4K.

The third and final tool is utilized and reported with varying periodicity ranging from monthly to quarterly assessments while community facilitators work in the field. Each RANI community facilitator is classified on a scale of 1 (exceptional) to 3 (needs improvement) points on several measures, which assess programmatic and human resource related performance such as a community facilitators’ ability to deliver intervention content and respond to intervention feedback loops in a timely manner, need for supportive supervision, and community mobilization skills. This data is collected by the district implementation management team and analyzed using Microsoft Excel.

IFA supply and demand

In 2019, the RANI project’s implementation leadership team based in Bhubaneshwar, Odisha conducted an initial assessment to map the IFA supply chain, supply status and monitoring techniques in intervention villages before intervention delivery began. Using this information, they operationalized how to monitor IFA supply status, developed tentative ideas for streamlining IFA supply in the intervention villages, and designed an IFA supply and demand monitoring module for RPM4K. IFA supply status is measured as the total number of supply points reporting adequate supply, shortage of supply or stock-outs. Similarly, IFA demand is measured as the total number of supply points reporting an increase in demand. IFA supply and demand is summarized at the village-level by algorithms embedded within RPM4K.

Hemoglobin levels

Hemoglobin testing gets conducted monthly among 15 women, using a HemoC ue photometer (model HB 301), which provides instant results in terms of hemoglobin concentration in gm/dL (grams per deciliter). This group consists of five women who volunteered as repeat testers every month and ten women who have not been tested before through the RANI project. Test results are shared as color coded (green – anemia free; yellow – mild anemia; orange – moderate anemia; red – severe anemia) blood shaped cards visualizing severity of anemia or lack thereof. Post testing, recommendations for behavioral actions and nudges are tailored according to participant’s test results. Measurement and monitoring of anemia levels at the village-level are analyzed and visualized using embedded algorithms and heat maps on RPM4K subsequently to monitor trends at the village-level.

Identifying, documenting and implementing feedback loops

Generally, process evaluation data are collected and analyzed at the end of the intervention delivery period, which limits the understanding and insights stakeholders have on barriers and facilitators on the ground⁸. The merger of feedback loops with applications such as RPM4K allow complex, multi-component interventions like RANI to assess ground-realities to innovate intervention delivery strategies in near-to-real-time. To do this systematically and efficiently, for every scenario that emerges, the M&E team first identifies the tailoring variables⁴⁸. They are defined as variables that serve as indicators of when an intervention delivery strategy requires optimization or calibration by the intervention delivery team. For the RANI project, the optimization or calibration takes the form of feedback loops. Table 3 provides several examples of hypothetical scenarios, measures, tailoring variables, and potential feedback loops identified to explicate this process in practice. All feedback loops are currently documented in an offline tool and will be built on as a formal module of RPM4K during future upgrades to the application (see Online Repository to review the RANI Project’s Feedback Loops Documentation Tool).

Tailoring variables for the RANI Project primarily focus on:

     1] Supportive supervision to bolster community facilitator performance. In general, a group of 7-8 RANI community facilitators are supported by a cluster supervisor. Overall, the supervisor helps the community facilitator gain mastery over the session content by organizing field level demonstrations. The supervisor provides on-site support in mobilizing participants and delivering intervention sessions. They provide constructive feedback to the community facilitator for improving session quality and participation levels based on both direct observation of session delivery and interaction with community members. Any discussion with policy makers and sections of the village which are unwilling to participate in the intervention or coordination with local health care officials are also facilitated with support from cluster supervisors.

Beyond cluster supervisors, the intervention management team also conducts monitoring visits and provides feedback to the field teams for improving intervention delivery. All broader issues identified in the field are shared during monthly meetings. Protocols and audio recordings for improving intervention delivery in local vernacular are developed for community facilitators and cluster supervisors on a regular basis. The entire intervention delivery team is also part of WhatsApp groups where updates, plans, requests for technical assistance for day-to-day activities are shared by all team members with instant feedback by the intervention management team.

     2] Refining RANI comm strategies. Informed by video receptivity and views data on RPM4K, we assess how each video resonates with our program participants. For example, if we find that adolescents are more receptive to the videos than older women, we pivot to show videos at events where adolescents are more likely to attend (e.g., a festival or school-related event) and increase other intervention activities among older women.

     3] Altering hemoglobin testing strategies.We alter our implementation plans based on hemoglobin testing data. For example, when we find that there is a high demand for hemoglobin testing in our intervention villages, we review quantitative and qualitative reports to examine how test results are being perceived and if they are influencing IFA use. If the reports indicate that hemoglobin testing is having the desired effect, we consider increasing the number of women that we test while remaining mindful of resource constraints.

     4] Policy advocacy to accommodate IFA supply or demand disruption. In the Angul district of Odisha, the government provides daily IFA supplementation for pregnant women and weekly IFA for adolescents for free. While adolescents and pregnant women can acquire IFA free of cost, non-pregnant women of reproductive age remain uncovered. Keeping this coverage gap in mind, the RANI project intervention team assesses and shares requirements for providing IFA supplements to non-pregnant women with local officials and frontline workers on a monthly basis. Every month after village-level hemoglobin testing is completed, aggregated test results are shared with local health care workers and higher-level administration at the intervention sites. This information is supplemented with a demand for IFA as per the aggregated anemia levels of pregnant and non-pregnant women of reproductive age in the intervention villages. IFA stock is usually replenished and delivered within a fortnight. In case of any delay or shortfall, the issue is escalated first at the block level and then to the district level, if required.

     5] Engaging in participatory consensus building with all stakeholders. The decision-making process we’ve adopted is participatory and based on 360-degree feedback from all key stakeholders. The intervention plan for a month is developed during monthly meetings of the intervention team with feedback incorporated from the process evaluation sub-team prior to seeking feedback from our intended program participants and local stakeholders. Next, the village-specific intervention plans are shared with women of reproductive age in our intervention villages, members of local influential groups, policy makers (sarpanch, ward members etc.) and frontline workers before finalization. Once the feedback loops are implemented, RANI community facilitators solicit comments from the participants on the session content and facilitation quality. Comments are also sought by supervisors and the intervention management team during monitoring visits via informal opinion polls. The quantitative ratings are recorded in RPM4K’s mobile-based forms, whereas qualitative feedback is compiled in monthly qualitative reports shared with all stakeholders. Field-level findings, intervention achievements and qualitative findings are shared with the process evaluation team on a weekly basis. These discussions, along with interaction with the field teams, is used to tweak RANI’s intervention delivery strategy on a monthly basis.

Monthly coordination and advocacy meetings are also conducted with block and district level government counterparts from the Departments of Health, Women and Child and Odisha Livelihood Mission. Village-wise hemoglobin status along with demand for IFA tablets is discussed during these interactions. The district collector (local government leadership) holds quarterly review meetings with all officials from the local departments involved with RANI. Through these interactions, local government officials also provide feedback and suggestions for improving the coordination and convergence of the RANI project with other ongoing anemia and health improvement programs in the district.

Survey data triangulation and analysis

Beyond using feedback loops to inform RANI intervention tweaks, we plan to triangulate RANI’s endline survey data with our monitoring data to assess whether intervention delivery factors (such as village-level reach, community facilitator performance over time, testing coverage, implementation coverage, IFA supply and demand) influence intervention exposure levels at the village-level. We will also explore whether and how 1) these intervention delivery factors influence psychosocial (e.g., self-efficacy, knowledge, awareness, risk perception), normative or behavioral (e.g., IFA use and adherence) outcomes at the village-level controlling for village-level demographics (i.e., age, income, education, caste) and structural factors (e.g., proximity to supply points). Finally, we will assess whether intervention exposure at the village-level mediates the relationship between village-level intervention delivery factors and psychosocial, normative, or behavioral outcomes.

Study status

The RANI study was implemented between September 2018 and March 2021,

Underlying data

Figshare. The RANI Project Process Monitoring and Evaluation Dataset and Codebook. DOI: https://doi.org/10.6084/m9.figshare.16709452.v1⁴⁹.

This project contains the following underlying data:

Extended data

Figshare. RPM4K Supplementary Files. DOI: 10.6084/m9.figshare.16782877

This project contains the following extended data:

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC BY 4.0 Public domain dedication).