Linkage of Chronic Disease Data from Provincial Sources for Strategic Decision Support and Population Health Surveillance in British Columbia (BC)
Bibliographic record
Abstract
IntroductionBC Ministry of Health (MoH)’s health administrative data holdings for a variety of general health care data are not readily linked with various data registries maintained by specialized care agencies of the Provincial Health Services Authority (PHSA). These provincial data sources have rich chronic disease information for BC residents. Objectives and ApproachThe objective of this project is to develop a system for cross-agency linkage of provincial level chronic disease data to improve chronic disease information that would support the BC’s health system, MoH and PHSA agencies in particular, in healthcare delivery and chronic disease prevention planning. We aim to achieve linkage of data from various provincial chronic disease data sources of the MoH and PHSA, with further potential to link with variety of other external databases such as Census data for socio-economic determinants of health. We are reporting here the outcome of the first phase of this project. ResultsThe outcomes from the project to date were as follows: Data linkage between the MoH’s administrative databases, Chronic Disease Registries (CDRs) in particular and Census based socio-economic status (SES) data was achieved, providing the population level evidence of health outcomes such as health inequity, comorbidities and multimorbidities (sub-project # 1). Preliminary results on data quality and health outcomes by SES will be presented. This was followed by completion of securing approval to ensure data security compliance for data linkages of CDRs with the Provincial Renal Agency’s Registry called “PROMIS” (sub-project # 2), Cardiac Services BC’s Registry called “HEARTis” ((sub-project # 3), and BC Cancer Agency’s Registry and BC Generations Project data (sub-project # 4), for implementation to answer agency specific research questions. Conclusion/ImplicationsThis data linkage project to consolidate information from chronic disease and socio-economic databases for providing answers to various analytic questions posed will improve decision support and enhanced population health surveillance. The lessons learned from this multi-agency collaboration and their implications for other jurisdictions will be addressed.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".