The state of integrated disease surveillance globally: synthesis report of a mixed methods study
Bibliographic record
Abstract
OBJECTIVES: Disease surveillance is an essential public health function needed to prevent, detect, monitor and respond to health threats. Integrated disease surveillance (IDS) enhances its utility and has been advocated for decades by the World Health Organization. This study sought to examine the state of IDS implementation worldwide. STUDY DESIGN: The study used a concurrent mixed methods approach consisting of a systematic scoping review of the literature on IDS, a survey of International Association of National Public Health Institutes (IANPHI) members and qualitative deep dive case studies in seven countries. METHODS: This report collates, analyses and synthesises the findings from the three components. The scoping review consisted of a review of summarised evidence on IDS. Eight reviews and five primary studies were included. The cross-sectional survey was conducted of 110 IANPHI members representing ninety-five countries. Qualitative case studies were conducted in Malawi, Mozambique, Uganda, Pakistan, Canada, Sweden, and England, which involved thirty-four focus group discussions and forty-eight key informant interviews. RESULTS: In the different countries, IDS is conceptualised differently and there are differing levels of maturity of IDS functions. Although the role of National Public Health Institutes has not been well defined in the IDS, they play a significant role in IDS in many countries. Fragmentation between sectors and resourcing (human and financial) issues were common. Good governance measures such as appropriate legislative and regulatory frameworks and roles and responsibilities for IDS were often unclear. The COVID-19 pandemic has strengthened some surveillance systems, often through leveraging existing respiratory surveillance systems. In some instances, improvements were seen only for COVID-19 related data but these changes were not sustained. Evaluation of IDS was also reported to be weak. CONCLUSIONS: Integration should be driven by a clear purpose and contextualised. Political commitment, clear governance, and resourcing are needed. Technology and the establishment of technical communities of practice may help. However, the complexity and cost of integration should not be under-estimated, and further economic and impact evaluations of IDS are needed.
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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 | 0.245 | 0.378 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.029 | 0.035 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".