Linking big data for cardiovascular health surveillance – opportunities and challenges using the CANHEART cohort
Bibliographic record
Abstract
IntroductionOur increasing ability to link large population-based health administrative datasets to create ‘big data’ cohorts offers unique opportunities to conduct health and health services surveillance at lower costs than traditional methods using surveys or primary data collection. However, comparability of findings from big data with traditional methods is unknown. Objectives and ApproachIn the CArdiovascular HEalth in Ambulatory Care Research Team (CANHEART) ‘big data’ initiative, we linked 19 population-based health databases to obtain baseline and 5-year follow-up health information on a cohort of 9.8 million adult residents of Ontario, Canada as of January 2008. We compared cardiovascular risk factor prevalence with results from 3500 participants in the 2007-09 Canadian Health Measures Survey (CHMS), a traditional population health surveillance survey. Additionally, we determined cardiovascular preventative care use and clinical event rates by sex and age. Planned linkages to new data sources will enable continued cohort surveillance of population health-related and care indicators. ResultsCholesterol and glucose levels determined from the CANHEART cohort were comparable to the CHMS, whereas blood pressure values and obesity rates were substantially higher. Overall, receipt of cardiovascular preventive care in the CANHEART cohort was high, with 85.7% of males and 91.8% of females having blood pressure assessments, and 67.8% of males and 79.4% of females having weight assessments. Cholesterol and diabetes screening rates among those recommended for screening were over 75%. Incidence of myocardial infarction, stroke or cardiovascular death was 51% higher among males than females (3.8 and 2.5 events per 1000 person-years, respectively). Challenges encountered in analyzing data included treatment of repeated and time-varying measures, selection of valid diagnostic and physician billing codes, changing coding practices and handling of missing and outlying data. Conclusion/ImplicationsComparability of cardiovascular risk factor prevalence using linked administrative data with survey methods varies by indicator. Selection biases amongst survey participants and different measurement methods could explain discrepancies. The added ability to examine health care indicators longitudinally and by subgroup supports use of linked population-based data to enhance health surveillance.
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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 | 0.126 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".