Innovative use of data sources: A Cross-sectional study of Data Linkage Practices across European Countries
Bibliographic record
Abstract
Abstract Background The availability of data generated from different sources is increasing with the possibility to link these data sources together. However, linked administrative data can be complex to use and may require advanced expertise and skills in statistical analysis. The main objectives of this study were to describe the current use of data linkage at the individual level and the artificial intelligence (AI) in routine public health activities, and to identify the related health outcome and intervention indicators and determinants of health for non-communicable diseases. Method We performed a survey across European countries to explore the current practices applied by national institutes of public health and health information and statistics for innovative use of data sources (i.e., the use of data linkage and/or the AI). Results The use of data linkage and the AI at national institutes of public health and health information and statistics in Europe varies. The majority of European countries use data linkage in routine by applying a deterministic method or a combination of two types of linkages (i.e., deterministic & probabilistic) for public health surveillance and research purposes. The use of AI to estimate health indicators is not frequent at national institutes of public health and health information and statistics. Using linked data, 46 health outcome indicators related to seven health conditions, 34 indicators related to determinants and 23 to health interventions were estimated in routine. Complex data regulation laws, lack of human resources, skills and problems with data governance, were reported by European countries as obstacles to link different data sources in routine for public health surveillance and research. Conclusions Our results highlight that the majority of European countries have integrated data linkage in routine public health activities but a few use the AI. A sustainable national health information system and a robust data governance framework allowing to link different data sources are essential to support evidence-informed health policy development process. Building analytical capacity and awareness of the added value of data linkage in national institutes is necessary for improving the utilization of linked data in order to improve the monitoring of public health activities.
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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 | 0.070 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".