DASH-EM (Digital AMR Surveillance Hub through Evidence Mapping)

We propose a digital solution that would leverage state-of-the-art cloud technologies and data pipelines to monitor AMR trends in the community using groundwater and surface water surveillance and predict the same utilizing data across one health sector. Evidence and gap maps, systematic reviews, and qualitative research support scalability across contexts.

Dr Giridara Gopal Parameswaran, Director, Centre for Digital and Public Health

Antimicrobial resistance (AMR) is a serious global public health issue of the 21st century that is predicted to cause over 10 million deaths in 2050 (1). A multi-sectoral, coordinated approach across the one health sectors is recommended to curtail the spread of AMR pathogens (2). The Southeast Asian region has the highest burden attributable to bacterial AMR, accounting for roughly 390,000 deaths and 16.1 million DALYs across all ages (3). India has one of the world's highest antimicrobial resistance rates (4) in addition to being one of the largest consumers of antibiotics (5) with no respite in reversing the trend (6).

India lacks an integrated antimicrobial resistance and antimicrobial use surveillance system across One Health (7). Current AMR estimates from India and other developing countries are primarily hospital-based, and thus understanding the burden of the issue at the community level is a major challenge (8). There are no Evidence and Gap Maps (EGM) on measuring AMR in the world currently. 

We thus propose a holistic solution that would leverage digital technologies and evidence mapping for compiling data across sectors to capture AMR trends in the community and guide policymakers on the best practices for their context.

Public health and policy stakeholders in human and animal health, clinicians, hospital authorities, and district authorities will be provided access to the dashboard and its reports. The stakeholders mentioned above can rely on the community trends of AMR to take suitable stewardship measures in addition to policy measures. The model will be able to quantify whether such measures have worked or not. More importantly, the system will be able to quantify the effect of each sector in the AMR evolution so that relevant stakeholders can take appropriate and timely measures. 

While the dashboard can be viewed as a hub for tracking AMR trends, there are potential off-shoot applications we foresee. The system can also be used as an outbreak detection system and help policymakers in planning drug procurements and distributions. This can serve as a means for monitoring high-quality timely data collection (IVRS can ensure timeliness whereas quality can be checked by random secondary calls). 

The EGM and qualitative research guide policymakers in the current project to prepare checklists and guidance documents. Collaboration with policymakers is integral for both the proposed plan as well as contextualized scale-up. Ultimately, this initiative aims to empower LMICs with evidence-driven strategies for addressing AMR.

The technology proposed in the project will enable stakeholders and policymakers to capture AMR trends, inform effective approaches to measure AMR through EGM, and implement appropriate stewardship. The proposed model in the backend captures animal, human, and environmental data along with treatment and prescription practices in the target region. By capturing a rising trend in AMR effective and data-driven policies can be devised for the population's well-being on a real-time basis. 

The core advantages of this project include 1) an automated data collation, 2) data management, and 3) Real-time analysis which ensures 4) minimal human intervention and thus 5) saving costs to the government. The proposed technology also ensures quality checks through an IVRS chatbot that would reach out to relevant stakeholders if there is any missing or incomplete data. The checklist and guidance documents that we propose to compile would help in understanding the contextual factors thereby providing a ready guide for implementation across the state, country, and region. The dataset and the technology would be provided to the government with minimal maintenance costs. We would at least publish four peer-reviewed publications that summarise the burden, our experience in health systems, and policy research and implementation.

https://drive.google.com/drive...

We believe that capturing the ground water trends of AMR coupled with GIS support can help us in capturing the representative community burden. In addition, we believe our solution coupled with EGMs would be able to contextualize the solution and be able to provide a justifiable and acceptable manner to scale and incorporate the solution in existing health systems.

The data points would include primarily individuals from the rural areas and thus equity and inclusivity would be addressed by our solution. We have mentioned the use and the potential of the impact of our solutions in previous sections.

In year 1, we plan to do the following:

1) Stakeholder engagement: We will actively engage in preparing the protocol, tools and the necessary system that they feel is easy and useful for implementation

In the subsequent years we plan to

1) Share our results across different states and various stakeholders to enable them device strategies and accept our solution.

We have mentioned other details of our stakeholder engagement and other activites in our GANTT chart.

The reduction in AMR will be impact goal. To capture this we will be looking into

1) The trend of AMR captured from humans (both resistant pathogen as well as antimicrobial use).

2) Also we will document evidences of policymakers using our dashboard for their planning and effective stewardship.

We have identified the following areas of potential challenges and have outlined respective risk mitigation strategies:

1) Stakeholder buy-in: We will ensure effective stakeholder engagement as mentioned before using our previous contacts and collaboration.

2) Data reliability. Availability/ accessibility/ timeliness /measurement biases/ validation and quality assurance are potential issues here. We will use a team of epidemiologists and data scientists to dissect the data sources and prepare a list of solutions that can be shared with the stakeholders. In addition, our dashboard will alert relevant individuals who are collating data if any data point is missing or incorrect.

3) Scale-up: We will leverage our existing contacts and the support from Trinity to boost our chances. We will also present the data findings to the stakeholders in a customized manner as we believe that each views data differently. First scale-up will be attempted within different districts in a state, and in different states across pan India and then we will contact health bodies in Nepal, Bangladesh, and China from our previous collaboration. Scaling up is a barrier without the right support. We have proposed collaborators with whom we think can help us scale and endorse the project.  

We aim to combat the serious threat of antimicrobial resistance (AMR) using the latest state-of-the-art digital technologies such as Cloud-based data pipelines, IVRS, Chatbots, EGMs, real-time interactive dashboards, and secondary data analysis. Our approach is innovative as it is the first of its kind to adopt cloud-based technology, IVRS, and EGMs in the area of AMR surveillance.

We believe that the Trinity challenge will provide us with the necessary launchpad to replicate our already tested solutions in AMR and also to ensure effective implementation of the project through strategic collaborations through its global network. We also believe that Trinity Challenge can help in facilitating our navigation through health systems for data access and necessary permission over and above our existing partnerships and liaisons. Our technology could not be taken to scale previously due to funding challenges and thus we see a wonderful opportunity to test our technology in the AMR domain and generate impact. This EGM is the first of its kind providing us as well as the stakeholders with crucial data on measurements and surveillance systems. We foresee our technology to be a one-of-a-kind outbreak/pandemic detection system which is an extension of its current functionality to track AMR.

We would like to collaborate with the Indian Council of Medical Research (ICMR), New Delhi, and its National Institute of Epidemiology in Chennai to facilitate access to existing surveillance datasets. This collaboration would help to derive all the disease burden data carried out under the IHIP platform and also refine our methodology. This will help in the initiation phase of the project when the research team and the software team brainstorm while deploying the cloud-based technology we propose. We have previous work experience with ICMR on evidence generation for anemia prevention programs in India and JHPIEGO on COVID-19 and Tuberculosis research.

With regards to ground implementation and scale-up across India, we would like to collaborate with JHPIEGO and Clinton Health Access Initiative (CHAI) as they are working across multiple state governments and understand the local contexts and health systems.

For expansion within Southeast Asia, we propose partnerships with the Bangladesh Rural Advancement Committee (BRAC) Institute of Governance and Development, and the London School of Hygiene and Tropical Medicine (LSHTM) with whom we already have established research collaboration.