Linkage, Evaluation and Analysis of National Electronic Healthcare Data: Application to Providing Enhanced Blood-Stream Infection Surveillance in Paediatric Intensive Care
Bibliographic record
Abstract
BACKGROUND: Linkage of risk-factor data for blood-stream infection (BSI) in paediatric intensive care (PICU) with bacteraemia surveillance data to monitor risk-adjusted infection rates in PICU is complicated by a lack of unique identifiers and under-ascertainment in the national surveillance system. We linked, evaluated and performed preliminary analyses on these data to provide a practical guide on the steps required to handle linkage of such complex data sources. METHODS: Data on PICU admissions in England and Wales for 2003-2010 were extracted from the Paediatric Intensive Care Audit Network. Records of all positive isolates from blood cultures taken for children <16 years and captured by the national voluntary laboratory surveillance system for 2003-2010 were extracted from the Public Health England database, LabBase2. "Gold-standard" datasets with unique identifiers were obtained directly from three laboratories, containing microbiology reports that were eligible for submission to LabBase2 (defined as "clinically significant" by laboratory microbiologists). Reports in the gold-standard datasets were compared to those in LabBase2 to estimate ascertainment in LabBase2. Linkage evaluated by comparing results from two classification methods (highest-weight classification of match weights and prior-informed imputation using match probabilities) with linked records in the gold-standard data. BSI rate was estimated as the proportion of admissions associated with at least one BSI. RESULTS: Reporting gaps were identified in 548/2596 lab-months of LabBase2. Ascertainment of clinically significant BSI in the remaining months was approximately 80-95%. Prior-informed imputation provided the least biased estimate of BSI rate (5.8% of admissions). Adjusting for ascertainment, the estimated BSI rate was 6.1-7.3%. CONCLUSION: Linkage of PICU admission data with national BSI surveillance provides the opportunity for enhanced surveillance but analyses based on these data need to take account of biases due to ascertainment and linkage error. This study provides a generalisable guide for linkage, evaluation and analysis of complex electronic healthcare data.
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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 | 0.236 | 0.406 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".