Community-Centered AMR Surveillance and Stewardship (CASS)

CASS: Community-Centered AMR Surveillance & Stewardship. A network of trained community health workers, mobile data collection, point-of-care diagnostics, and analytics empowering communities to fight antibiotic resistance with targeted interventions, improved stewardship, and data-driven policy.

Shakirah Nandawula

The solution targets a critical deficiency in the global fight against antimicrobial resistance (AMR): the dearth of accurate, community-level data in low- and middle-income countries (LMICs).

This creates a major barrier to the implementation of effective control measures. Globally, AMR causes an estimated 4.95 million deaths per year, a number rising rapidly. LMICs face a compounded burden, where higher infectious disease rates often meet with poorly regulated antibiotic use and underdeveloped healthcare systems.

The causes of this crisis are multifaceted, including antibiotic overuse in humans and animals, reliance on presumptive treatments due to lack of diagnostics, inadequate surveillance systems, the prevalence of substandard medications, and community behaviors driven by limited knowledge. 

CASS tackles these problems head-on. By generating granular community data, integrating point-of-care diagnostics, fostering targeted behavioral change, and building robust surveillance, the solution aims to improve prescription practices, reduce antibiotic misuse, and enable smarter policy-making decisions on the frontlines of the AMR battle.

• Communities in LMICs:

  • Needs: Access to accurate information about proper antibiotic use, better diagnostics for infections, reduced spread of resistant bacteria, affordable and effective medications.
  • CASS addresses these needs through awareness campaigns based on locally collected data, improved access to diagnostics, and overall guidance for health behaviors that minimize AMR.

• Community Health Workers (CHWs):

  • Needs: Tools for data collection, evidence-based guidance on antibiotic use, educational resources to effectively work with their communities.
  • CASS provides CHWs with the mobile app for data collection, educational materials, and access to locally-informed treatment decision support

• Local Healthcare Providers (Doctors, Pharmacists):

  • Needs: Rapid diagnostic tools, up-to-date information on local antibiotic resistance trends, support in providing targeted care, reducing over prescription.
  • CASS facilitates access to point-of-care diagnostics, delivers local AMR insights directly on the dashboard, and reinforces data-backed antibiotic guidelines.

• Policymakers (Local/Regional Health Ministries):

  • CASS delivers comprehensive community-level data visualizations, identifies hotspots for targeted actions, and can assist in impact assessments through continuous monitoring.

The solution prioritizes Surveys, focus groups and collaboration with CHWs, local doctors, and community leaders in early project stages to shape how the system interacts with its users and the real-world context.

CASS aims to provide several essential public goods directly tied to combating the AMR crisis.

 Firstly, it would generate a granular, open-access dataset, specific to community antibiotic use in LMICs. This de-identified data on medication patterns, resistance trends, and infections would address a massive information gap for global researchers. By building this foundational evidence base, the goal is to enable focused interventions that address the most urgent needs within LMICs. 

Secondly, CASS aims to provide evidence-based guidelines for frontline healthcare workers focusing on rational antibiotic prescription. These guidelines would adapt continuously based on data collected, directly reducing antibiotic misuse in locations with limited diagnostic resources. 

Lastly, the system would offer a best-practices toolkit for creating tailored AMR awareness campaigns. These would employ adaptable templates informed by locally gathered insights into social factors driving antibiotic consumption. This addresses the vital element of public understanding and promotes stewardship on an individual level. All materials would be provided in readily understandable formats in at least one language widely spoken in the participating LMICs with the potential for additional translation to ensure widespread accessibility.

The core goal is to provide communities with better health outcomes by giving them tools to understand and fight antibiotic resistance. For frontline health workers like community health workers (CHWs) and pharmacists, the solution means access to data-driven guidelines, improving their ability to prescribe the right treatments. Local health officials will finally have information tailored to their region, so they can put limited resources exactly where they're needed most. But CASS impact goes beyond healthcare providers: by building understanding about how communities use antibiotics, the system helps create awareness campaigns that actually resonate with people and change behaviors at the individual level.

For the global fight against AMR, the solution becomes a game-changer. Researchers focused on LMICs will finally have hard data to design the kind of interventions that will work in these places.

Most importantly, CASS is about helping those who often get overlooked. Because antibiotic resistance hits hardest in communities with poor healthcare and low health awareness, the benefits of CASS will disproportionately help the most vulnerable. It's about gathering critical information where it's missed, using data to target resources effectively, and helping individuals make decisions that slow the spread of superbugs.

Year 1: Establishing the Foundation

• Focus: Pilot in a select number of LMIC communities, partnered with local health authorities and organizations. The goal is to refine every part of the system -- how data is collected, how the platform works, and most importantly, making sure the insights it produces are meaningful and usable to those on the ground.
• Partners are Key: Working closely with on-the-ground practitioners and community leaders in the pilot ensures CASS evolves in the right direction.
• Success Metric: It's not just about gathering data, but using it. Did local clinics change their prescribing patterns? Did communities respond to awareness campaigns?

Year 2-3: Expansion and Refinement

• Scaling to new regions within the target LMICs. Different locations mean different challenges, making the dataset incredibly rich and revealing the true diversity of issues driving AMR.
• The system is only as impactful as the people using it. It's crucial to invest in training local data scientists and giving health officials tools to use the platform on their own.
• Early evidence of success within the pilot regions becomes powerful advocacy to expand collaboration and secure further resources.

Data System Performance

• Reliability: Data completeness and accuracy rates compared to baseline before deployment.
• Track CHW data entry times, error rates, and gather their feedback on ease of use to refine app design.
• Diagnostic Integration: Percentage of diagnoses successfully linked to diagnostic results to track point-of-care implementation in health facilities.

Impact on Antibiotic Use

• Percentage change in prescription of critically important antibiotics within CASS communities compared to control regions.
• Diagnostics-guided Prescriptions: In clinics with point-of-care testing, the increase in prescriptions aligned with test results (vs. before its use).
• Community Understanding: Pre- and post-survey change in knowledge and behavior scores related to antibiotics in areas targeted with CASS awareness campaigns.

Policy Uptake & Resource Allocation

• Data Access & Usage: Web analytics on health officials' interactions with the platform.
• Informed Decisions: Number of resource allocation initiatives (e.g., mobile clinics, targeted drug availability) aligning with insights provided by CASS.
• Advocacy Evidence: Early outcomes from CASS communities used to build the case for wider national initiatives for data-backed AMR policies.

Long-term AMR Surveillance

•  Ability to detect early outbreaks of resistant strains (compared to regional reporting timelines).
• AMR Maps: Granularity of available community-level antibiotic resistance maps over time, aiding the identification of potential 'hotspots.

Year1 Barriers

• Technical Feasibility:
  • Challenge: Ensuring data collection app works reliably with varied mobile technology and connectivity in LMIC communities.
  • Mitigation: Extensive field testing in the pilot areas, partnering with local developers for tailored solutions, offline data entry capabilities where needed.
• Community Engagement:
  • Challenge: Mistrust of data collection, concerns around privacy, or reluctance to adopt new processes by busy health workers.
  • Mitigation: Strong partnerships with respected local organizations, emphasizing the tangible benefits, transparency about data security, and ensuring the system genuinely assists the workflow, not creates burdens.
• Limited Resources:
  • Challenge: Pilot stage funding restricts deployment scale and access to advanced computational resources needed to develop sophisticated AI models.
  • Mitigation: Prioritize core functionalities, leverage open-source technologies, seek grants focused on LMIC innovation, explore collaborations with tech-focused universities.

Year2-3 Barriers

• Scaling Challenges
  • Challenge: Adapting to new regions with diverse languages, health systems, and unique AMR complexities.
  • Mitigation: Modular design for the platform, building capacity for local analytics customization, investing in knowledge exchange across CASS project sites.

• Sustainability:
  • Challenge: The reliance on ongoing funding creates vulnerability and potentially limits adoption by resource-strapped regions.
  • Mitigation: Explore hybrid models. Public-private partnerships may sustain some core components, offering tiered access models to support affordability.

The Trinity Challenge would be a unique opportunity for us because of its:

• Increased visibility: As an early-stage project, recognition from the Trinity Challenge would open doors to funding, potential partnerships with healthcare institutions, and the attention of key stakeholders who could support implementation.
• Potential for funding: Securing funds through the Challenge would directly address the major barriers we face.

Specific Barriers The Trinity Challenge Can Help Overcome

1. Data Access: Sourcing high-quality, standardized medical image data sets is costly and time-consuming. -Collaboration opportunities within the Trinity Challenge network could unlock essential data for improving our detection accuracy.

2. Scaling: We need additional computing resources and personnel to expand our algorithm's capacity and make it broadly deployable. Funding and connections from the Challenge would make wider implementation of our early detection tool a reality.

We shall seek partnership with Google, Bill and Melinda Gates Foundation, Facebook etc and this partnership shall solely be for the following goals.

• Peer-to-Peer Networking
• Organizational Mentorship
• Impact Measurement Validation and Support
• Media Visibility and Exposure
• Grant Funding
• Develop prototypes of the chatbot tool adapted for use among different types of semi-skilled health workers
• Introduce an effective referral system across stakeholders within primary healthcare
• Form partnerships with relevant stakeholders, such as local governments and the private sector, to implement chatbot tools on the ground in sub-Saharan Africa and South Asia