The state of integrated disease surveillance in seven countries: a synthesis report
Bibliographic record
Abstract
Integrated disease surveillance (IDS) offers the potential for better use of surveillance data to guide responses to public health threats. However, the extent of IDS implementation worldwide is unknown. This study sought to understand how IDS is operationalized, identify implementation challenges and barriers, and identify opportunities for development. Synthesis of qualitative studies undertaken in seven countries. Thirty-four focus group discussions and 48 key informant interviews were undertaken in Pakistan, Mozambique, Malawi, Uganda, Sweden, Canada, and England, with data collection led by the respective national public health institutes. Data were thematically analysed using a conceptual framework that covered governance, system and structure, core functions, finance and resourcing requirements. Emerging themes were then synthesised across countries for comparisons. None of the countries studied had fully integrated surveillance systems. Surveillance was often fragmented, and the conceptualization of integration varied. Barriers and facilitators identified included: 1) the need for clarity of purpose to guide integration activities; 2) challenges arising from unclear or shared ownership; 3) incompatibility of existing IT systems and surveillance infrastructure; 4) workforce and skills requirements; 5) legal environment to facilitate data sharing between agencies; and 6) resourcing to drive integration. In countries dependent on external funding, the focus on single diseases limited integration and created parallel systems. A plurality of surveillance systems exists globally with varying levels of maturity. While development of an international framework and standards are urgently needed to guide integration efforts, these must be tailored to country contexts and guided by their overarching purpose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".