Strengthening a collaborative approach to implementing surveillance systems: Lessons from the Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP) in Malawi and Zimbabwe
Bibliographic record
Abstract
Abstract Background With the number of antimicrobials available to effectively treat gonorrhoea rapidly diminishing, surveillance of antimicrobial–resistant Neisseria gonorrhoeae (NG) is critical for global public health security activity. Many low-and-middle-income countries (LMICs) have gaps in their existing sexually transmitted infections (STIs) surveillance systems that negatively impact global efforts geared towards achieving the United Nations (UN) Sustainable Development Goals (SDGs). This paper explains the contribution of collaborative surveillance systems to health systems strengthening (HSS) learning from integrating NG surveillance into existing Ministries of Health’s (MoH) antimicrobial resistance (AMR) surveillance services in Malawi and Zimbabwe. Methods We used the WHO Enhanced Gonococcal Antimicrobial Surveillance Programme (EGASP) implementation experiences in Malawi and Zimbabwe to demonstrate the collaboration in AMR and STI surveillance. We conducted qualitative interviews with purposively selected health managers directly participating in the AMR and STI programs using a standardized key informant guide to describe how to plan for a collaborative surveillance system. Qualitative thematic analysis was conducted to delineate stakeholders’ recommendations using the health systems’ building blocks. Results Stakeholder engagement, prioritization of needs, and power to negotiate were key drivers to a successful collaborative surveillance system. Weak governance, policies, lack of accountability, and different priorities, coupled with weak collaborations, workforce, and health information systems, were challenges faced in having effective collaborative surveillance systems. Data availability, use, and negotiation power were key drivers for the prioritization of collaborative surveillance. Including collaborative surveillance in primary health services and increasing government budget allocation for surveillance were recommended. Conclusions Strengthening collaborative surveillance systems in LMICs using a people-centered approach increases transparency and accountability and empowers national institutions, communities, and stakeholders to engage in sustainable activities that potentially strengthen health systems. EGASP implementations in Zimbabwe and Malawi serve as models for other countries planning to implement or improve collaborative surveillance systems in their context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".