Hospital variation and cesarean delivery: Studies of contemporary practice patterns in Canada and the United States
Bibliographic record
Abstract
In Canada, 27% of deliveries were performed by cesarean in 2013, making Cesarean delivery the most commonly performed surgery in Canadian women.Given that high rates of cesarean have not been shown to be associated with better maternal and perinatal outcomes, there have been recommendations to reduce the overall rate of cesarean delivery, with a focus on preventing the first ("primary") cesarean.To best determine how to reduce the rate of primary cesarean delivery, an understanding of indication-specific patterns of cesarean is needed.The goal of this thesis was to advance our understanding of indication-specific cesarean delivery rates through the use of inter-institutional practice variation.To explore contemporary practice patterns in Canada, this thesis used data across three provincial birth registries, and focused on low-risk nulliparous women delivered following the onset of labour.Practice patterns in the timing of cesarean delivery by indication are illustrated, and an examination of adherence to clinical guidelines on the management of labour is presented.We find that many cesarean deliveries are performed early during labour and demonstrate substantial variation across hospitals in their compliance with clinical guidelines on the management of labour.Next, inter-hospital variation in the rates of cesarean delivery for labour dystocia is examined.After stabilization for hospital size and adjustment for maternal, fetal, and hospital characteristics, variability in rates across hospitals remained high.Together, these findings suggest that hospitals with the highest rates may benefit from conducting internal reviews and examining adherence to best practice guidelines on the management of labour dystocia.This thesis then examines women who had a previous cesarean delivery using data from the United States' Nationwide Inpatient Sample.We find that the occurrence of a uterine rupture at a hospital is associated with a subsequent reduction to the hospital's trial of labour success rate (that is, the rate of women having a successful vaginal delivery, in those who underwent labour) and a short-term increase in the rate of repeat cesarean delivery.These findings suggest that obstetrical decision-making is impacted by the occurrence of rare, adverse events.My thesis has been shaped and guided by two wonderful co-supervisors, Dr. Jay Kaufman and Dr. Jennifer Hutcheon.Together, these expert epidemiologists have provided me with superb guidance, methodological and substantive.I'd like to thank Jay for being the apostle who relayed Modern Epidemiology in mostly bite-sized pieces, and from whom I've learned a great deal about epidemiologic methods, in particular multi-level modeling with an econometric flair and the critical issues about analyses concerning race as a causal factor.You've been an excellent mentor, and I am very grateful for the countless e-mail exchanges, and meetings during walks through the Plateau that helped to bring this document to fruition.I was also very fortunate that Jenn agreed to take me on as a trainee, and provide such excellent inter-provincial guidance.I have learned a great deal about how to tailor my papers for a clinical readership and my thesis benefitted considerably from your focus on the practical conclusions of this work.Beyond the scope of this thesis, you strived to include me in important side-projects that encouraged me to step out of my research silo (more accurately, my research basement) and enhance my understanding beyond the textbook and journal article to support the goal of clinically meaningful work.My thesis committee was made complete by Dr. Erin Strumpf
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| 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".