A more accurate approach to define abortion cohorts using linked administrative data: an application to Ontario, Canada
Bibliographic record
Abstract
Background: The shifting landscape of abortion care from a hospital-only to a distributed service including primary care has implications for how to identify abortion cohorts for research and surveillance. The objectives of this study were to 1) create an improved approach to define abortion cohorts using linked administrative data sets and 2) evaluate the performance of this approach for abortion surveillance compared with standard approaches. Methods: We applied four principles to identify induced abortion cohorts when some services are delivered beyond hospital settings; 1) exclude early pregnancy losses and postpartum procedures; 2) use multiple data sources; 3) define episodes of care; 4) apply a hierarchical algorithm to determine abortion date to a population-based cohort of all abortion events in Ontario (Canada) from January 1, 2018-March 15, 2020. We calculated risk differences (RD, with 95% confidence intervals) comparing the proportion of medication vs. surgical, first vs. second trimester, and complication incidence applying these principles vs. standard approaches. Results: Hospital-only data (versus multiple data sources) underestimated the frequency of medication abortion (16.1% vs. 31.4%; RD -15.3% [-14.3, -16.3]) and first-trimester abortion (82.1% vs. 94.5%; RD -12.8 [-11.4, 13.4]) and overestimated incidence of abortion complication (2.9% vs. 0.69%; RD 2.2% [1.8, 2.7]). An unlinked (versus linked) approach underestimated the frequency of abortion complications (0.19% vs 0.69%, -RD 0.50% [-0.44--0.56]). Including (versus excluding) abortions following early pregnancy loss or delivery events increased the estimated incidence of abortion complications (1.29% vs. 0.69%, RD 0.60% [0.51-0.69]. Conclusion: New methods are required to accurately identify abortion cohorts for surveillance or research. When legal or regulatory approaches to medication abortion evolve to enable abortion in primary care or office-based settings, hospital-based surveillance systems will become incomplete and biased; to continue valid and complete abortion surveillance, methods must be adjusted to ensure complete capture of procedures across all settings.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".